diff --git a/README.md b/README.md
index 7d1bc2d..e84493a 100644
--- a/README.md
+++ b/README.md
@@ -47,7 +47,7 @@ For more installation information refer to these [installation instructions](doc
 
 ### Computational requirements
 
-The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, `n_batches` of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this [tutorial](docs/tutorials/01_getting_started_fermionic.ipynb), those calls are inside a `for` loop. **It is highly recommended to perform these calls in parallel**.
+The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, `n_batches` of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this [tutorial](docs/tutorials/01_chemistry_hamiltonian.ipynb), those calls are inside a `for` loop. **It is highly recommended to perform these calls in parallel**.
 
 The [`qiskit_addon_sqd.fermion.solve_fermion()`](qiskit_addon_sqd/fermion.py) function is multithreaded and capable of handling systems with ~25 spacial orbitals and ~10 electrons with subspace dimensions of ~$10^7$, using ~10-30 cores.
 
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new file mode 100644
index 0000000..e12a1a0
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diff --git a/docs/_static/images/sqd_diagram.png b/docs/_static/images/sqd_diagram.png
new file mode 100644
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diff --git a/docs/how_tos/benchmark_pauli_projection.ipynb b/docs/how_tos/benchmark_pauli_projection.ipynb
index 1752539..68f6b17 100644
--- a/docs/how_tos/benchmark_pauli_projection.ipynb
+++ b/docs/how_tos/benchmark_pauli_projection.ipynb
@@ -130,13 +130,9 @@
     }
    ],
    "source": [
-    "import time\n",
-    "\n",
-    "import numpy as np\n",
-    "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli_string, sort_and_remove_duplicates\n",
-    "\n",
-    "\n",
-    "def connected_element_and_amplitude_bool(x, diag, sign, imag):\n",
+    "def connected_element_and_amplitude_bool(\n",
+    "    x: bool, diag: bool, sign: bool, imag: bool\n",
+    ") -> tuple[bool, complex]:\n",
     "    \"\"\"\n",
     "    Finds the connected element to computational basis state |x> under\n",
     "    the action of the Pauli operator represented by (diag, sign, imag).\n",
@@ -195,6 +191,9 @@
     }
    ],
    "source": [
+    "import numpy as np\n",
+    "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n",
+    "\n",
     "rand_seed = 22\n",
     "np.random.seed(rand_seed)\n",
     "\n",
@@ -240,31 +239,36 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 0 took 0.31216s\n",
-      "Iteration 1 took 0.510622s\n",
-      "Iteration 2 took 0.775817s\n",
-      "Iteration 3 took 1.047106s\n",
-      "Iteration 4 took 1.354705s\n",
-      "Iteration 5 took 1.583962s\n",
-      "Iteration 6 took 1.846798s\n",
-      "Iteration 7 took 2.072656s\n",
-      "Iteration 8 took 2.313123s\n",
-      "Iteration 9 took 2.539087s\n",
-      "Iteration 10 took 2.831971s\n",
-      "Iteration 11 took 3.149036s\n",
-      "Iteration 12 took 3.36273s\n",
-      "Iteration 13 took 3.661241s\n",
-      "Iteration 14 took 3.998323s\n",
-      "Iteration 15 took 4.310177s\n",
-      "Iteration 16 took 4.654591s\n",
-      "Iteration 17 took 4.686089s\n",
-      "Iteration 18 took 5.002513s\n",
-      "Iteration 19 took 5.188594s\n"
+      "Iteration 0 took 0.201246s\n",
+      "Iteration 1 took 0.348222s\n",
+      "Iteration 2 took 0.576333s\n",
+      "Iteration 3 took 0.78356s\n",
+      "Iteration 4 took 1.016162s\n",
+      "Iteration 5 took 1.305325s\n",
+      "Iteration 6 took 1.392751s\n",
+      "Iteration 7 took 1.632433s\n",
+      "Iteration 8 took 1.826521s\n",
+      "Iteration 9 took 2.02903s\n",
+      "Iteration 10 took 2.297458s\n",
+      "Iteration 11 took 2.588042s\n",
+      "Iteration 12 took 2.738746s\n",
+      "Iteration 13 took 2.906144s\n",
+      "Iteration 14 took 3.148833s\n",
+      "Iteration 15 took 3.323253s\n",
+      "Iteration 16 took 3.664171s\n",
+      "Iteration 17 took 3.680663s\n",
+      "Iteration 18 took 4.008313s\n",
+      "Iteration 19 took 4.173532s\n"
      ]
     }
    ],
    "source": [
-    "pauli_str = [\"Z\" for i in range(n_qubits)]\n",
+    "import time\n",
+    "\n",
+    "from qiskit.quantum_info import Pauli\n",
+    "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli\n",
+    "\n",
+    "pauli = Pauli(\"Z\" * n_qubits)\n",
     "\n",
     "# Different subspace sizes to test\n",
     "d_list = np.linspace(d / 1000, d, 20).astype(\"int\")\n",
@@ -275,7 +279,7 @@
     "for i in range(20):\n",
     "    int_bts_matrix = bts_matrix[: d_list[i], :]\n",
     "    time_1 = time.time()\n",
-    "    _ = matrix_elements_from_pauli_string(int_bts_matrix, pauli_str)\n",
+    "    _ = matrix_elements_from_pauli(int_bts_matrix, pauli)\n",
     "    time_array[i] = time.time() - time_1\n",
     "    print(f\"Iteration {i} took {round(time_array[i], 6)}s\")"
    ]
@@ -288,7 +292,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -359,31 +363,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 0 took 0.331874s\n",
-      "Iteration 1 took 0.604671s\n",
-      "Iteration 2 took 0.91632s\n",
-      "Iteration 3 took 1.219938s\n",
-      "Iteration 4 took 1.598329s\n",
-      "Iteration 5 took 1.9079s\n",
-      "Iteration 6 took 2.188979s\n",
-      "Iteration 7 took 2.530264s\n",
-      "Iteration 8 took 2.827899s\n",
-      "Iteration 9 took 3.154194s\n",
-      "Iteration 10 took 3.514766s\n",
-      "Iteration 11 took 3.83003s\n",
-      "Iteration 12 took 4.185016s\n",
-      "Iteration 13 took 4.514196s\n",
-      "Iteration 14 took 4.948467s\n",
-      "Iteration 15 took 5.388114s\n",
-      "Iteration 16 took 5.596917s\n",
-      "Iteration 17 took 5.776064s\n",
-      "Iteration 18 took 6.082537s\n",
-      "Iteration 19 took 6.356327s\n"
+      "Iteration 0 took 0.236567s\n",
+      "Iteration 1 took 0.424116s\n",
+      "Iteration 2 took 0.673399s\n",
+      "Iteration 3 took 0.905164s\n",
+      "Iteration 4 took 1.168936s\n",
+      "Iteration 5 took 1.454204s\n",
+      "Iteration 6 took 1.74778s\n",
+      "Iteration 7 took 1.920795s\n",
+      "Iteration 8 took 2.259994s\n",
+      "Iteration 9 took 2.550674s\n",
+      "Iteration 10 took 2.681287s\n",
+      "Iteration 11 took 3.04411s\n",
+      "Iteration 12 took 3.293262s\n",
+      "Iteration 13 took 3.471247s\n",
+      "Iteration 14 took 3.726639s\n",
+      "Iteration 15 took 4.072854s\n",
+      "Iteration 16 took 4.221037s\n",
+      "Iteration 17 took 4.498535s\n",
+      "Iteration 18 took 4.741108s\n",
+      "Iteration 19 took 5.159038s\n"
      ]
     }
    ],
    "source": [
-    "pauli_str = [\"Z\" for i in range(n_qubits)]\n",
+    "pauli = Pauli(\"Z\" * n_qubits)\n",
     "\n",
     "# Different subspace sizes to test\n",
     "d_list = np.linspace(d / 1000, d, 20).astype(\"int\")\n",
@@ -398,7 +402,7 @@
     "    int_bts_matrix = bts_matrix[: d_list[i], :]\n",
     "    int_row_array = row_array[: d_list[i]]\n",
     "    time_1 = time.time()\n",
-    "    _ = matrix_elements_from_pauli_string(int_bts_matrix, pauli_str)\n",
+    "    _ = matrix_elements_from_pauli(int_bts_matrix, pauli)\n",
     "    time_array[i] = time.time() - time_1\n",
     "    print(f\"Iteration {i} took {round(time_array[i], 6)}s\")"
    ]
@@ -411,7 +415,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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9F7t3777tpJTWtLvdJI23e/2tUb9+fSQnJ2PTpk34/fff8fvvvyMmJgaPP/444uLibDpXdnY2+vTpA41Gg/nz56N58+bw8vJCUlISXn75Zat7SgCYj33hhRdu25vQokULm+qz1e0+UxV91kpqX7x4MTp16lTmsbd+Dio6p0qlwvbt27F161Zs2LABGzduxOrVq9G/f3/88ccft21vq8r8zJYM3Pfz8yvVvn79+gCKg1dISAgaNWoEo9GIq1evmvcBQGFhIa5fv47GjRtXw7NAtfysVGVS1Mr+HiDnYY8TVYlMJsOCBQtw8eJFfPzxx+bt/v7+Zd6VVPI/28pw5IzNzZs3R2ZmJgYMGICBAweW+rLmf61jxozBxo0bkZubi9WrVyMsLAw9e/YsdVzPnj3x9ttvY//+/fjmm29w5MgRfPfddxWev7x2Jf/7v/U9uN3rbzKZSl0WTUlJAQCLS1AKhQLDhg3Dp59+itOnT2PKlCn46quvcOrUKYu2JT0pJfLy8nDp0iXzubZt24br168jNjYWs2bNwgMPPICBAweWusGgefPmAIDDhw/f9nUo6SmTy+VlvlcDBw60uFx2q9DQUFy6dKnU3V0nTpy4bZvqUvL8NBrNbWuvzFxLHh4eGDBgAN577z0cPXoUb7/9NrZs2XLbS8yhoaEAyn7Ox48fR7169eDt7W1zHWXV1alTJ2RkZJTqTbp48SIAIDAwEADMQXL//v0Wx+3fvx8mk+m2QbPkHCqVqtTnECj9HG39WQFKf75PnToFk8lU7vxMFf3+quzvAXI8Bieqsr59+6J79+744IMPzLfkNm/eHMePH0dGRob5uIMHD1Z4Z095Sn5xlxXIqtuYMWNw4cIFfP7556X26XS6UpctyjJ27Fjo9XrExcVh48aNpea4ysrKKvW/85I/BuV101vTLjQ0FDKZDNu3b7c47tNPP73teW8OvkIIfPzxx5DL5RgwYACA4nEpN/Pw8ECHDh3KrPezzz6zGK+xdOlSFBUVYciQIQD+11tx8/MoLCwsVV+XLl3QtGlTfPDBB6Xe95K29evXR9++fbF8+XJcunSp1PO6+TNYlvvvvx9FRUVYunSpeZvRaHTIDNXh4eFo3rw5/u///q/M2/Irqr0smZmZpbZV9Llq1KgROnXqhLi4OIvX+fDhw/jjjz9w//3321zH7YwdOxZGo9Gil7KgoADffPMN2rZta+5J6t+/PwICAizeF6D4s6RWqzF06NDbPoZMJsOgQYPw448/mi/9AcV3gW7atMniWI1Gg3r16tn0s1IyNUqJks9Kyee7LLf7/VXZ3wPkPLxUR9XixRdfxOjRoxEbG4upU6di4sSJeO+99zBo0CBMmjQJV69exbJly9CuXbtyB4+WJzw8HAAwc+ZMDBo0CDKZDI888kh1Pg2zxx57DN9//z2mTp2KrVu3IiIiAkajEcePH8f333+PTZs2lbpN+lZdunRBixYt8Nprr0Gv11tcpgOAuLg4fPrpp3jooYfQvHlz3LhxA59//jk0Gk25f6isaefr64vRo0djyZIlkCQJzZs3x6+//nrbsT5eXl7YuHEjIiMj0aNHD/z+++/YsGEDXn31VXMPwOTJk5GZmYn+/fsjKCgIaWlpWLJkCTp16mS+vbpEYWEhBgwYgDFjxuDEiRP49NNPcdddd+HBBx8EAPTu3Rv+/v6IjIzEzJkzIUkSvv7661J/QDw8PLB06VIMGzYMnTp1QnR0NBo1aoTjx4/jyJEj5j+Cn3zyCe666y60b98eTzzxBJo1a4YrV65g165dSE9Px8GDB2/7eg4bNgwRERF45ZVXkJqairZt22LdunWlxhbZg4eHB1asWIEhQ4agXbt2iI6ORpMmTXDhwgVs3boVGo0Gv/zyi03nnD9/PrZv346hQ4ciNDQUV69exaeffoqgoCDzjQllWbx4MYYMGYJevXph0qRJ5ukIfH19q3WpoylTpmDFihWYMWMGUlJSEBISgq+//hppaWkWz1WlUuHNN9/EjBkzMHr0aAwaNAg7duzAypUr8fbbb1sMYi/LvHnzsHHjRtx9992YPn06ioqKsGTJErRr1w7//POPxbGTJ0/GO++8g8mTJ6Nr167Yvn27uce1LGfPnsWDDz6IwYMHY9euXVi5ciUeffRRdOzY8bZtOnXqBJlMhoULFyInJwdKpRL9+/fHqlWrKvV7gJzICXfykZsqudW8rNtmjUajaN68uWjevLn5dvWVK1eKZs2aCYVCITp16iQ2bdp02+kIFi9eXOqcuOVW4KKiIvH000+LwMBAIUmSxS3Ftx5bctvwrbdkR0ZGCm9v71KP1adPH9GuXTuLbYWFhWLhwoWiXbt2QqlUCn9/fxEeHi7mzZsncnJyyn2tSrz22msCgGjRokWpfUlJSWLcuHEiJCREKJVKUb9+ffHAAw+I/fv3l3tOa9tlZGSIUaNGCbVaLfz9/cWUKVPE4cOHy5yOwNvbW5w+fVrcd999Qq1WiwYNGog5c+YIo9FoPm7NmjXivvvuE/Xr1xcKhUKEhISIKVOmiEuXLpmPKfmMxMfHiyeffFL4+/sLHx8fMX78eIvb3IUQIiEhQfTs2VOoVCrRuHFj8dJLL4lNmzaVecv233//Le69915Rp04d4e3tLTp06CCWLFlicczp06fF448/Lho2bCjkcrlo0qSJeOCBB8SaNWvKfT2FEOL69eviscceExqNRvj6+orHHntMHDhwwOrpCGbMmGGx7Xaf65JpIn744QeL7QcOHBAjR44UdevWFUqlUoSGhooxY8aIv/76q9Rj3/qZLnnNS6Y4+Ouvv8Tw4cNF48aNhUKhEI0bNxbjxo0TKSkppeq79Rb8P//8U0RERAiVSiU0Go0YNmyYOHr0qMUx1tZRnitXrojIyEgREBAglEql6NGjh9i4cWOZx3722WeidevWQqFQiObNm4v333/fYuqG8sTHx4vw8HChUChEs2bNxLJly8p8D7VarZg0aZLw9fUVderUEWPGjBFXr1697e+Vo0ePiocffljUqVNH+Pv7i6eeekrodDqLc946HYEQQnz++eeiWbNm5ikRtm7dWunfA+Q8khBVHH1LRPSv2NhYREdHY9++fRX2yBE5w9y5czFv3rwq33hCtRfHOBERERFZicGJiIiIyEoMTkRERERW4hgnIiIiIiuxx4mIiIjISgxORERERFZy6wkwTSYTLl68iDp16jh0OQ4iIiKqOYQQuHHjBho3bgwPj/L7lNw6OF28eLHCxVaJiIiIrHH+/HkEBQWVe4xbB6eShTvPnz8PjUbj5GqIiIjIHeXm5iI4OLjcBcFLuHVwKrk8p9FoGJyIiIioSqwZ9sPB4URERERWYnAiIiIishKDExEREZGVGJyIiIiIrMTgRERERGQlBiciIiIiKzE4EREREVmJwYmIiIjISgxORERERFZicCIiIiKyEoMTERERkZUYnIiIiIis5NaL/BIREVHNJoRAltaAfH0RvJWe8FfLrVqM114YnIiIiMjl5OgMWJuYjridqUjL1Jq3hwaoEdk7DKPCg+Crkju8LkkIIRz+qNUkNzcXvr6+yMnJgUajcXY5REREVA3iUzIwbWUidIVGAMDNQaWkr0mlkGHphHD0aRVY5cezJU9wjBMRERG5jPiUDETH7IXOYISAZWjCv98LADqDEdExexGfkuHQ+hiciIiIyCXk6AyYtjKxOBxVcD1MiOIANW1lInJ0BkeUB4DBiYiIiFzE2sR06AqNFYamEkIAukIj1iWl27ewmzA4ERERkdMJIRC3M7VSbWMTUuGoIdsMTkREROR0WVoD0jK1pcY0VUQASMvUIlvrmMt1DE5ERETkdPn6oiq1z6tie2sxOBEREZHTeSurNrWkTxXbW4vBiYiIiJzOXy1HaIAats4JLqF4Ukw/tWMmw2RwIiIiIqeTJAljuwXbPMYJAKIiwhy2DAuXXCEiIiKnO52Rh9X7ztvUxkMCvOQyjOwSZKeqSmNwIiIiIqfaeuIqZn57ADcKihCgliNbZ6hwEsySDqZlE8IdumYdL9URERGRUwgh8Nn205gUuw83CorQNdQfm57tg5jo7lDJZZCAUmOeSrap5DLERnfHPdWwVp0t2ONEREREDldgMGL2ukNYf+ACAOCRbsGYP/xOKDw90KdOIHbNHoB1SemITUhFWqbW3C4kQI2oiDCMCg+CxstxPU0lJOGoqTbtwJbVjImIiMg1XMktwJNfJ+Lg+WzIPCS88UBbPN4rtMwB3kIIZGsNyNMXwUfpCT+1vNoHgtuSJ9jjRERERA6TfD4bT361H1dv6OGnluOTR7sgokW92x4vSRL8vRXw91Y4sMrbY3AiIiIih1h/IB0vrz2EwiITWjXwweePd0VoXW9nl2UTBiciIiKyK6NJYNHG41i+/QwAYGCbBvjgkU4Om+27OrlfxUREROQ2cnQGzPruALadyAAAPNWvBZ67txU8PBwzYWV1Y3AiIiIimwkhkKU1IF9fBG+lJ/zLGLR9JiMPk7/ajzMZ+fCSe2Dxwx0xrGNjJ1VcPRiciIiIyGo5OgPWJqYjbqflNAGhAWpE9i6eJsBXJUd8SgaeWpWEGwVFaOTrhc8f74o7m/g6sfLqwekIiIiIyCrxKRmYtjIRukIjAFisK1fS16RSyDC8U2Os3nceJgGEh/pj2YRwBNZROrxea3E6AiIiIqpW8SkZiI7ZW7wUShn7S7ZpC434dm/xmnNjuwZj/oh2UHrKHFWm3XHJFSIiIipXjs6AaSsTK1w/7mZymYRX77+jRoUmgMGJiIiIKrA2MR26QqPVoQkAiowC6/5dTqUmYXAiIiKi2xJCIG5naqXaxiakwo2HUpeJwYmIiIhuK0trQFqmtsxxTeURANIytcjWGuxRltMwOBEREdFt5euLqtQ+r4rtXQ2DExEREd2WdxWXRXHHZVXK4/TgdOHCBUyYMAF169aFSqVC+/btsX//fmeXRURERAD81XKEBqhh6wIpEoonxfRTy+1RltM4NThlZWUhIiICcrkcv//+O44ePYp3330X/v7+ziyLiIiI/iVJEiJ7h1WqbVREWKllWNydU/vPFi5ciODgYMTExJi3NW3a1IkVERER0a16NQ+w6XgPCfCSyzCyS5CdKnIep/Y4/fzzz+jatStGjx6N+vXro3Pnzvj8889ve7xer0dubq7FFxEREdlPypUbiIrZZ76rrqL+o5IOpmUTwuGrqlmX6QAnB6czZ85g6dKlaNmyJTZt2oRp06Zh5syZiIuLK/P4BQsWwNfX1/wVHBzs4IqJiIhqj8S0LIxetgtXcvVo1cAHH4ztBJVCBgmlA1TJNpVchtjo7rinVaDjC3YApy7yq1Ao0LVrV+zcudO8bebMmdi3bx927dpV6ni9Xg+9Xm/+Pjc3F8HBwVzkl4iIqJptPXEV01cmQWcwokuIH76M6gY/tQI5OgPWJaUjNiEVaZla8/GhAWpERYRhVHgQNF7u1dPkNov8NmrUCG3btrXY1qZNG6xdu7bM45VKJZRK111dmYiIqCb48cAFvPDDQRSZBPq2DsSn47tArSiODL4qOaIjmiKqdxiytQbk6Yvgo/SEn1pe4waCl8WpwSkiIgInTpyw2JaSkoLQ0FAnVURERFS7xSScxbxfjgIARnRqjMWjO0IuKz2yR5Ik+Hsr4O+tcHSJTuXU4PTss8+id+/e+O9//4sxY8Zg7969+Oyzz/DZZ585sywiIqJaRwiB9zanYMmWUwCA6IgwvD60LTw8an4vki2cOsYJAH799VfMnj0bJ0+eRNOmTfHcc8/hiSeesKqtLdckiYiIqGxGk8DrPx3Gqj3nAAAv3NcKM/q1qBWX3gDb8oTTg1NVMDgRERFVjb7IiGdXJ+O3Q5fhIQFvjWiPR3uEOLssh3KbweFERETkPHn6Ijz51X7sPH0dCpkHPnykE4a0b+TsslwagxMREVEtdD1Pj6iYfTh0IQfeChk+f7wrereo5+yyXB6DExERUQ0jhECW1oB8fRG8lZ7wv2WqgPQsLR7/Yi/OXMtHXW8FYqO7o32QrxMrdh8MTkRERDVEjs6AtYnpiNtZenLKyN7Fk1NeyS3AY1/swZVcPZr4qfD1pO5oFujjxKrdCweHExER1QDxKRmYtjIRukIjAODmP+4lfU0KTw/IPCRoC41o1cAHX03sgYa+Xg6v1dVwcDgREVEtEp+SgeiYvRCwDEwlSrbpi0wAgBaB3vh+Si/4qWvX5JXVwamL/BIREVHV5OgMmLYysTg0WXkN6UJOQa2Zo6m6MTgRERG5sbWJ6dAVGq0OTQBQUGjEuqR0+xVVgzE4ERERuSkhBOJ2plaqbWxCKtx4mLPTMDgRERG5qSytAWmZ2jLHNZVHAEjL1CJba7BHWTUagxMREZGbytcXVal9XhXb10YMTkRERG7KW1m1m+N9qti+NmJwIiIiclN+Kk8E+ihtbieheFJMP7W8+ouq4Rg1iYiI3NCeM9excONxZOTpK9U+KiKMUxJUAoMTERGRGzl2KReLNh7H1hMZAAAvTw8UmQSMJmHVIHEPCfCSyzCyS5B9C62hGJyIiIjcwPlMLd7fnIL1yRcgBCDzkDCuezBm9m+JY5dvIDpmL4DyJ8Es6WBaNiEcvipepqsMBiciIiIHEUIgS2tAvr4I3kpP+KvlFV4uu56nx8dbT+Gb3edQaCxeMmVoh0Z44b7WaFrPGwBQX+OFmOjuFa5Vp5LLsGxCOO5pFVjdT63WYHAiIiKysxydAWsT0xG3MxVpmVrz9tAANSJ7h2FUeFCpHqB8fRFW7DiLz3ecMU8bcFeLenh58B1oH+Rb6jH6tArErtkDsC4pHbEJlo8TEqBGVETx42i82NNUFZJw42lDbVnNmIiIyBniUzIq7glSyLB0Qjj6tApEYZEJ3+49hyVbTuJaXiEAoH0TX7w8+A7c1bKeVY8phEC21oA8fRF8lJ7ws6JnqzazJU+wx4mIiMhO4lMyEB2zt3gB3jL2l2zTGYyIjtmLJ+9pht8OXca5f3uLwuqq8fx9rTG0fSN4eFgffCRJgr+3Av7eiio/B7LE4ERERGQHOToDpq1MLA5NFVzbEaI4RC2LPwMAqOejxKyBLfFIt2DIZZxy0ZUwOBEREdnB2sR06AqNNq8j1791ID4e3wVqBf9EuyLGWCIiomomhEDczlSb20kATmfkQyWXVXtNVD0YnIiIiKpZltaAtEytzb1NAkBaphbZWoM9yqJqwOBERERUzfL/nT6gsvKq2J7sh8GJiIiomnkrqzY+yaeK7cl+GJyIiIiqmb9ajpAAtc3tJBRPiumn5iSVrorBiYiIqJodvpALw7/Lo9gqKiKMk1W6MPYFEhERVZM8fRHe+yMFsTvPwvTvyHAJZU9+eSsPCfCSyzCyS5A9S6QqYnAiIiKqBn8cuYw5Px/BpZwCAMCDHRtjQJv6eHZ1MoDyJ8Es6WBaNiG81Jp15FoYnIiIiKrgYrYOc38+gj+OXgFQvKDuWyPuxD2tAgEAfmpFxWvVyWVYNiHc3IZcF4MTERFRJRQZTYjblYb3/jiB/EIjPD0kTOnTDE/3bwmvmyaw7NMqELtmD8C6pHTEJqQi7d916IDikBUVEYZR4UHQeLGnyR1IQlS0go7rsmU1YyIiovIIIZClNSBfXwRvpSf81fLbDtI+lJ6D2ev/weELuQCArqH++O/I9mjVoE6Fj5GtNSBPXwQfpSf8ynkMchxb8gR7nIiIqFbL0RmwNjEdcTste4NCA9SI7F3cG1Qy7ihPX4R3/ziBuJ2pMAlA4+WJ2fe3wdiuwfDwqDgASZIEf28F/L0Vdns+ZF/scSIiolorPiWj4vFHChmWTghHgcGIOT8dweXc4sHfIzo1xmtD2yKwjtKxRVO1Y48TERFRBeJTMhAdsxcCZU8XULJNV2hE5Jd7zdtD6xYP/r67JQdy10YMTkREVOvk6AyYtjKxODRVcN3l5t1P3NUUzw9qbTH4m2oXBiciIqp11iamQ1dotGpiyhISgMb+KoamWo5LrhARUa0ihEDcztRKtY1NSIUbDw2masDgREREtUqW1oC0TK1NvU1A8SW7tEwtsrUGe5RFboLBiYiIapV8fVGV2udVsT25NwYnIiKqVbyVVRve61PF9uTeGJyIiKhW8VfLERqghq3zdUsonhTTT82lUWozBiciIqpVJElCZO+wSrWNigjjEim1HIMTERHVOqPCg6BSWD+tgIdUPIP4yC5BdqyK3AGDExER1Tq+KjkGtWto1bElHUzLJoSb16yj2ovBiYiIap2/T17Dj8kXAAAKmQckoNSYp5JtKrkMsdHdcU8rLrFCnDmciIhqmcs5BZj13QEIATzSLRiz72+DdUnpiE1IRVqm1nxcSIAaURFhGBUeBI0Xe5qoGIMTERHVGkVGE57+NgnX8wvRtpEGcx9sBy+5DNERTRHVOwzZWgPy9EXwUXrCTy3nQHAqhcGJiIhqjcV/nMC+1Cz4KD3x6fguFuvOSZIEf28F/L0VTqyQXB3HOBERUa2w+egVLI8/AwBY/HAHhNXzdnJF5I4YnIiIqMY7n6nF898nAwCiI8IwpH0j5xZEbovBiYiIajR9kREzViUht6AInYL9MHtIG2eXRG6MwYmIiGq0tzccwz/pOfBTy/HJ+C5QePJPH1UePz1ERFRj/XLwIr7alQYAeH9sJzTxUzm5InJ3DE5ERFQjnc7Iwytr/wEAzOjXHP1a13dyRVQTMDgREVGNoys0YvrKJOQXGtGjaQCeHdjK2SVRDcHgRERENc7rPx3GiSs3UM9HiSXjOsNTxj93VD34SSIiohrl+/3nsSYxHR4S8NG4Tqiv8XJ2SVSDMDgREVGNcexSLl7/8TAA4Ll7W6F383pOrohqGgYnIiKqEW4UGDD9myToi0zo2zoQ0/u2cHZJVAMxOBERkdsTQuCVtYdw9lo+Gvt64f0xneDhwQV6qfoxOBERkdv7alcaNhy6BE8PCUse7cKFesluGJyIiMitJZ/PxlsbjgIAZt/fBuGh/k6uiGoyBiciInJb2dpCzPgmCQajwOB2DTExIszZJVEN59TgNHfuXEiSZPF1xx13OLMkIiJyQUIIZOYX4nymFpn5hRBCwGQSeP77g7iQrUNoXTUWje4ASeK4JrIvT2cX0K5dO/z555/m7z09nV4SERG5iBydAWsT0xG3MxVpmVrz9tAANVrU98Ffx69C4emBT8d3gcZL7sRKqbZwekrx9PREw4YNnV0GERG5mPiUDExbmQhdobHUvnOZWnOQGt8jBO0a+zq6PKqlnD7G6eTJk2jcuDGaNWuG8ePH49y5c7c9Vq/XIzc31+KLiIhqnviUDETH7IXOYIQAIG7Zf/P3cTtTEZ+S4cDqqDZzanDq0aMHYmNjsXHjRixduhRnz57F3XffjRs3bpR5/IIFC+Dr62v+Cg4OdnDFRERkbzk6A6atTCwOTLcmpjIIANNWJiJHZ7B3aUSQhLDmY+kY2dnZCA0NxXvvvYdJkyaV2q/X66HX683f5+bmIjg4GDk5OdBoNI4slYiI7OTLv8/izV+PluplKo8E4I1hbREd0dReZVENlpubC19fX6vyhNMv1d3Mz88PrVq1wqlTp8rcr1QqodFoLL6IiKjmEEIgbmdqpdrGJqTChfoCqIZyqeCUl5eH06dPo1GjRs4uhYiInCBLa0Baptam3iag+HJdWqYW2VperiP7cmpweuGFFxAfH4/U1FTs3LkTDz30EGQyGcaNG+fMsoiIyEny9UVVap9XxfZEFXHqdATp6ekYN24crl+/jsDAQNx1113YvXs3AgMDnVkWERE5ibeyan+WfKrYnqgiTv2Efffdd858eCIicjH+ajlCA9Q4Z+PlOglASIAafmpOgkn25VJjnIiIqHaTJAmRvcMq1TYqIoxLrpDdMTgREZFLGRUeBC+5zOrjPSRApZBhZJcgO1ZFVIzBiYiIXIrGyxOdgq1bQqWkg2nZhHD4qniZjuyPwYmIiFzKyt1p2HUmEx4SoPT0gITiMUw3K9mmkssQG90d97TiTUXkGLz9gIiIXEby+WzM//UoAGD2kDYY0y0Y65LSEZuQal7UFygeCB4VEYZR4UHQeLGniRyHwYmIiFxCVn4hZnyTBINRYHC7hph8d1NIkoToiKaI6h2GbK0Befoi+Cg94aeWcyA4OQWDExEROZ3JJPDM6mRcyNYhrK4ai0Z3sAhGkiTB31sBf2+FE6sk4hgnIiJyAUu2nEJ8Sga85B5YOiGcl9/IZTE4ERGRU8WnZOCDv1IAAG+NaI82jbiAO7kuBiciInKaC9k6PPPdAQgBjOsejIfDORcTuTYGJyIicorCIhNmfJOELK0BdzbRYM6wds4uiahCDE5EROQUb284iuTz2dB4eWLp+HCbZgsnchYGJyIicrifD15E3K40AMD7YzshOEDt5IqIrMPgREREDnXyyg28svYfAMD0vs0xoE0DJ1dEZD0GJyIicph8fRGmfZMEbaERvZvXxXP3tnJ2SUQ2YXAiIiKHEELglXWHcOpqHhpolPhoXGd4yvhniNwLP7FEROQQX+1Kwy8HL8LTQ8Inj3ZBPR+ls0sishmDExER2V3SuSy8taF48d5XhtyBrmEBTq6IqHIYnIiIyK4y8wvx1L+L997fviEm3dXU2SURVRqDExER2Y3RJDDruwO4mFOAZvW8sXCU5eK9RO6GwYmIiOzmw79OYsfJa/CSe+DTCV1Qh4v3kptjcCIiIrvYduIqlmw5CQD470PtcUdDLt5L7s/T2QUQEZH7EkIgS2tAvr4I3kpP+KvlkCQJ6VlaPLM6GUIAj/YIwcguXLyXagYGJyIislmOzoC1iemI25mKtEyteXtogBrje4bg5+SLyNYa0L6JL954oK0TKyWqXgxORERkk/iUDExbmQhdobHUvnOZWvz3t+MAALVChk/Hd+HivVSjcIwTERFZLT4lA9Exe6EzGCEAiFv23/x9gcGIM9fyHVgdkf0xOBERkVVydAZMW5lYHJhuTUxlEACmrUxEjs5g79KIHIbBiYiIrLI2MR26QqNVoQkoDle6QiPWJaXbtzAiB2JwIiKiCgkhELcztVJtYxNSIaxNW0QujsGJiIgqlKU1IC1TW2pMU0UEgLRMLbK1vFxHNQODExERVShfX1Sl9nlVbE/kKhiciIioQt7Kqs1e41PF9kSugsGJiIgq5K+WIzRADVuX55VQPCmmn5pr1FHNwOBEREQVkiQJkb3DKtU2KiIMkmRr5CJyTQxORERklVHhQfCUWR+APCRApZBxnTqqURiciIjIKt/tPQeD0br76ko6mJZNCIevipfpqOZgcCIiogp9svUUFvxevAbdiM6NoVbIIAGlxjyVbFPJZYiN7o57WgU6uFIi++JtDkREVK6P/jqJ9zanAACev7cVnh7QEjk6A9YlpSM2IRVpmVrzsSEBakRFhGFUeBA0XuxpoppHEm48nWtubi58fX2Rk5MDjUbj7HKIiGoUIQTe//MkPvrrJADgpcGtMb1vi1LHZGsNyNMXwUfpCT+1nAPBye3YkifY40RERKUIIfDuHyn4eOspAMCr99+BJ+9pXuo4SZLg762Av7fC0SUSOQWDExERWRBCYOHGE1gWfxoA8PoDbTHprqZOrorINTA4ERGRmRAC//3tGD7fcRYAMO/BdpWev4moJmJwIiIiAMWhaf6vRxGTkAoAeHPEnXisZ6hziyJyMQxOREQEIQTm/HwEX+1KAwAsGNke47qHOLkqItfD4EREVMuZTAKv/3QY3+w5B0kCFo7sgDHdgp1dFpFLYnAiIqrFTCaBV9cfwnf7zkOSgP97uCNGhXOJFKLbYXAiIqqljCaBl9f+gzWJ6fCQgPfGdMKIzk2cXRaRS2NwIiKqhYwmgRd/OIh1By5A5iHhg7GdMKxjY2eXReTyGJyIiGooIQSytAbk64vgrfSE/7+zehcZTXj+h4P4KfkiPD0kfDSuM+5v38jZ5RK5BQYnIqIaJkdnwNrEdMTttFxHLjRAjQm9QrEvNRN/HLkCTw8JHz/aBYPvbOjEaoncC4MTEVENEp+SgWkrE6ErNJbady5Ti7c3HAMAyDyApRPCcW/bBo4ukcitMTgREdUQ8SkZiI7ZCwGgrNXbb94mBKDw9HBQZUQ1B39qiIhqgBydAdNWJhaHprJS0y0EgGkrE5GjM9i7NKIahcGJiKgGWJuYDl2h0arQBBSHK12hEeuS0u1bGFENw+BEROTmhBCI25laqbaxCakQ1qYtImJwIiJyd1laA9IytWWOayqPAJCWqUW2lpfriKzF4ERE5Oby9UVVap9XxfZEtQmDExGRm/NWVu0GaZ8qtieqTRiciIjcnL9ajtAANSQb20konhTTTy23R1lENRKDExGRm5MkCZG9w2we4wQAURFhkCRbIxdR7cX+WSKiGkBfVHqm8PJ4SICXXIaRXYLsVBFRzcQeJyIiN/fD/vNYuPEEgOLLbxV1IJXsXzYhHL4qXqYjsgWDExGRG/v90CW8vPYfAMCku5oiJrobVHJZcYC65diSbSq5DLHR3XFPq0AHV0vk/nipjojITcWnZGDmdwdgEsDYrsH4z9A2kCQJu2YPwLqkdMQmpCItU2s+PiRAjaiIMIwKD4LGiz1NRJUhCTeeMjY3Nxe+vr7IycmBRqNxdjlERA6z92wmHv9yDwoMJgzt0AgfPdIZMg/LPiYhBLK1BuTpi+Cj9ISfWs6B4ERlsCVPuMylunfeeQeSJOGZZ55xdilERC7t8IUcTIrdhwKDCf1aB+L9MZ1KhSag+G47f28FggPU8PdWMDQRVQOXCE779u3D8uXL0aFDB2eXQkTk0k5dvYHHv9yLG/oi9GgagKUTwqHwdIlf5US1gtN/2vLy8jB+/Hh8/vnn8Pf3d3Y5REQu63ymFuNX7EFmfiE6BvliRWRXeMllzi6LqFZxenCaMWMGhg4dioEDBzq7FCIil3UltwDjV+zBlVw9WjXwQWx0d9ThAG8ih3PqXXXfffcdkpKSsG/fPquO1+v10Ov15u9zc3PtVRoRkcvIyi/EY1/swblMLUIC1Fg5qQf8vRXOLouoVnJaj9P58+cxa9YsfPPNN/Dy8rKqzYIFC+Dr62v+Cg4OtnOVRETOdaPAgMiYvUi5koeGGi98M7kH6mus+51JRNXPadMR/Pjjj3jooYcgk/3v+rzRaIQkSfDw8IBer7fYB5Td4xQcHMzpCIioRiowGPH4l3ux92wmArwV+H5KT7SoX8fZZRHVOLZMR+C0S3UDBgzAoUOHLLZFR0fjjjvuwMsvv1wqNAGAUqmEUql0VIlERE5TWGTCtJWJ2Hs2E3WUnvhqYneGJiIX4LTgVKdOHdx5550W27y9vVG3bt1S24mIahOjSeDZ1cnYeiIDXnIPfBndDXc28XV2WUQEF7irjoiI/kcIgVfXHcKGQ5cgl0lY/lhXdAsLcHZZRPQvl1qrbtu2bc4ugYjI7oQQyNIakK8vgrfSE/7/LoUihMBbG45h9f7z8JCAjx7pjD5ciJfIpbhUcCIiqslydAasTUxH3E7LxXdDA9SI7B2GqzcK8MXfZwEAix7uiCHtGzmrVCK6DauC088//2zzie+9916oVCqb2xER1UTxKRmYtjIRukJjqX3nMrWY/+tR8/dzh7XFw+FBjiyPiKxkVXAaMWKETSeVJAknT55Es2bNKlMTEVGNEp+SgeiYvRAAypr/5eZtEoCmgT6OKYyIbGb14PDLly/DZDJZ9aVWq+1ZMxGR28jRGTBtZWJxaLJm1jwJmLYyETk6g71LI6JKsCo4RUZG2nTZbcKECZyQkogIwNrEdOgKjdaFJhSHK12hEeuS0u1bGBFVitNmDq8Otsz0SUTkaEII9F28DecytWVeorsdCUBIgBrbXuwLSZLsVR4R/cuWPFHleZxyc3Px448/4tixY1U9FRFRjZKlNSDNxtAEFI95SsvUIlvLy3VErsbm4DRmzBh8/PHHAACdToeuXbtizJgx6NChA9auXVvtBRIRuat8fVGV2udVsT0RVT+bg9P27dtx9913AwDWr18PIQSys7Px0Ucf4a233qr2AomI3JW3smpT5flUsT0RVT+bg1NOTg4CAoqn/9+4cSNGjRoFtVqNoUOH4uTJk9VeIBGRu/JXyxEaoIato5QkFE+K6aeW26MsIqoCm4NTcHAwdu3ahfz8fGzcuBH33XcfACArKwteXl7VXiARkbuSJAmRvcNsHuMEAFERYRwYTuSCbO4HfuaZZzB+/Hj4+PggNDQUffv2BVB8Ca99+/bVXR8RkdsSQuB8lrbiA2/iIQFechlGduHM4USuyObgNH36dPTo0QPnzp3DvffeCw+P4k6rZs2acYwTEdG/TCaB1386jG/2nANQfPkNUvmTYJZ0MC2bEA5fFS/TEbkizuNERFTNDEYTXvzhIH5MvghJAt4e0R5N/FUWa9XduswKAKgUMiybEI57WgU6vGai2qza53F67rnnkJ+fb3UBs2fPRmZmptXHExHVFAUGI6Z/k4Qfky/C00PCB2M74dEeIejTKhC7Zg/AG8PaIiTAclmqkAA13hjWFrtfHcDQROTirOpxkslkuHz5MgIDrfuB1mg0SE5Otvsiv+xxIiJXkq8vwpNf70fCqetQeHpg6fguGNCmQanjhBDI1hqQpy+Cj9ITfmo5B4ITOZEtecKqMU5CCLRq1crqH2xbeqeIiGqCHJ0B0TF7kXQuG94KGT6P7IrezeuVeawkSfD3VsDfW+HgKomoqqwKTjExMTafuEGD0v/LIiKqia7l6fHYF3tx7FIufFVyxEZ3Q+cQf2eXRUR2YFVwioyMtHcdRERu6WK2DhNW7MGZa/mo56PE15O6o00jDh0gqqk4nz8RUSWdvZaPCSv24EK2Dk38VFg5uQea1vN2dllEZEcMTkRElXD8ci4mrNiLa3l6NKvnja8n90ATP5WzyyIiO2NwIiKyUfL5bER+uRc5OgPaNNLgq4ndEVhH6eyyiMgBGJyIiGyw6/R1TI7bh/xCIzqH+CE2qjt8uRgvUa1h8yK/JU6dOoVNmzZBp9MBKJ6ygIjI3QkhkJlfiPOZWmTmF1r8bvvr2BVExuxFfqERES3qYuWkHgxNRLWMzT1O169fx9ixY7FlyxZIkoSTJ0+iWbNmmDRpEvz9/fHuu+/ao04iIrvK0RmwNjEdcTtTkZb5v4V5QwPUiOwdBm+lDK+tP4wik8DANg3w8aOd4SWXObFiInIGm3ucnn32WXh6euLcuXNQq/+3bMDYsWOxcePGai2OiMgR4lMy0GvBX3jz16M4d1NoAoBzmVrM//UoXl57CEUmgeGdGmPphC4MTUS1lM09Tn/88Qc2bdqEoKAgi+0tW7ZEWlpatRVGROQI8SkZiI7ZCwHLhXdL3LoY70OdmkAuq/QoByJyczb/9Ofn51v0NJXIzMyEUsm7SojIfeToDJi2MrE4NFkzTFMCpq9KQo7OYO/SiMhF2Ryc7r77bnz11Vfm7yVJgslkwqJFi9CvX79qLY6IyJ7WJqZDV2i0LjShOFzpCo1Yl5Ru38KIyGXZfKlu0aJFGDBgAPbv34/CwkK89NJLOHLkCDIzM5GQkGCPGomIqp0QAnE7UyvVNjYhFVG9w6xe+JyIag6be5zuvPNOpKSk4K677sLw4cORn5+PkSNH4sCBA2jevLk9aiQiqnZZWgPSMrVljmsqjwCQlqlFtpaX64hqo0pNgOnr64vXXnutumshInKYfH1Rldrn6Yvg762opmqIyF1UKjgVFBTgn3/+wdWrV2EymSz2Pfjgg9VSGBGRPXkrq7Zwgk8V2xORe7L5J3/jxo14/PHHce3atVL7JEmC0WislsKIiOzJXy1HaIAa52y8XCcBCAlQw48zhhPVSjaPcXr66acxevRoXLp0CSaTyeKLoYmI3IUkSYjsHWbzGCcAiIrgwHCi2srmHqcrV67gueeeQ4MGDexRDxGRwwT7q2w63kMCvOQyjOwSVPHBRFQj2dzj9PDDD2Pbtm12KIWIyHEOns/GM6uTzd9X1IFUsn/ZhHD4qniZjqi2koSwduq3YlqtFqNHj0ZgYCDat28PudzyF8jMmTOrtcDy5ObmwtfXFzk5OdBoNA57XCJyb6eu3sDoZbuQpTWgd/O6iI4Iw6zvkqErLB5ucOsyKwCgUsiwbEI47mkV6PB6ici+bMkTNgenL774AlOnToWXlxfq1q1rcZ1fkiScOXOmclVXAoMTEdkqPUuLh5fuwuXcAnQM8sU3T/SEj9ITOToD1iWlIzYhFWk3LfQbGqBGVEQYRoUHQePFniaimsiuwalhw4aYOXMmXnnlFXh4OHehSwYnIrLFtTw9Ri/bhbPX8tGivg9+mNKr1FxMQghkaw3I0xfBR+kJP7WcA8GJajhb8oTNg8MLCwsxduxYp4cmIiJb5BYY8PgXe3H2Wj6a+Knw9aTuZU5gKUkS/L0VnNySiMpkc/qJjIzE6tWr7VELEZFdFBiMmBy7H0cv5aKejwJfT+qORr623VFHRARUosfJaDRi0aJF2LRpEzp06FBqcPh7771XbcUREVWVwWjCjG+SsDc1E3WUnoiN7o5mgT7OLouI3JTNwenQoUPo3LkzAODw4cMW+zgOgIhcickk8OIPB/HX8atQenrgi6huuLOJr7PLIiI3ZnNw2rp1qz3qICKqVkIIzP/1KH5MvghPDwlLJ3RB96YBzi6LiNwcR3gTUY30wZ8nEbszFQDwf6M7ov8dXO2AiKrOqh6nkSNHIjY2FhqNBiNHjiz32HXr1lVLYURElRWTcBYf/nUSADDvwXYY0bmJkysioprCquDk6+trHr+k0Wg4lomIXNb6A+mY98tRAMCzA1shsneYcwsiohrF5gkwXQknwCSim/159AqmrEyE0SQQ1TsMc4a15X/0iKhCtuQJm8c49e/fH9nZ2WU+aP/+/W09HRFRtdh95jpmrEqC0SQwsnMTvPEAQxMRVT+b76rbtm0bCgsLS20vKCjAjh07qqUoIqJbCSGQpTUgX18Eb6Un/G9aCuXwhRxMjtsPfZEJA9vUx8KHO8DDg6GJiKqf1cHpn3/+Mf/76NGjuHz5svl7o9GIjRs3okkTDsAkouqVozNgbWI64naWXnw3sncYuob6Izp2H/L0RejeNAAfP9oFchlvGCYi+7A6OHXq1AmSJEGSpDIvyalUKixZsqRaiyOi2i0+JQPTViZCV2gste9cphZv/lo8CFwAuLOJBisiu8JLLnNwlURUm1gdnM6ePQshBJo1a4a9e/ciMDDQvE+hUKB+/fqQyfgLi4iqR3xKBqJj9kKgOBjd6tZtU+5pDo2XvIwjiYiqj9XBKTQ0FABgMpnsVgwREVB8eW7aysTi0GTFfb8SgJfX/oN7WgXCV8XwRET2w4EARORy1iamQ1dotCo0AcW9T7pCI9Ylpdu1LiIiBicicilCCMT9u1SKrWITUuHGU9MRkRtgcCIil5KlNSAtU1vmuKbyCABpmVpkaw32KIuICACDExG5mHx9UZXa51WxPRFReRiciMileCttnpfXgk8V2xMRlceq3zD+/v5WL12QmZlZpYKIqHbzV8sRGqDGORsv10kAQgLU8FPzrjoish+rgtMHH3xg5zKIiIpJkoTI3mHmyS1tERURxvXpiMiurApOkZGR9q6DiMjs/vYN8daGo1ZPR+AhAV5yGUZ2CbJvYURU61kVnHJzc60+oUajqXQxRER5+iLM/C4Zpn9Dk4SyZw4vUdLBtGxCOCe/JCK7syo4+fn5Vdj9LYSAJEkwGkuvKUVEZI0bBQZExexDYloW6ig9MWtgS7y3OcW8Vt3NAarkN5JKLsOyCeG4p1VgqfMREVU3q4LT1q1b7fLgS5cuxdKlS5GamgoAaNeuHd544w0MGTLELo9HRK4rR2vA4zF7cfB8NjRenvh6Ug90DPbD6K7BWJeUjtiEVKRlas3HhwSoERURhlHhQVyjjogcRhJOnGb3l19+gUwmQ8uWLYtnC46Lw+LFi3HgwAG0a9euwva5ubnw9fVFTk4OLxESubGs/EI89uUeHL6QCz+1HCsn9cCdTXwtjhFCIFtrQJ6+CD5KT/ip5RwITkTVwpY8UengpNVqce7cORQWFlps79ChQ2VOZxYQEIDFixdj0qRJFR7L4ETk/q7n6TF+xR4cv3wDdb0VWDm5B9o04s8zETmOLXnC5pniMjIyEB0djd9//73M/ZUd42Q0GvHDDz8gPz8fvXr1qtQ5iMi9XL1RgPGf78HJq3mo56PEt0/0QMsGdZxdFhHRbdk8c/gzzzyD7Oxs7NmzByqVChs3bkRcXBxatmyJn3/+2eYCDh06BB8fHyiVSkydOhXr169H27ZtyzxWr9cjNzfX4ouI3NPlnAI88tlunLyahwYaJVZP6cnQREQuz+Yepy1btuCnn35C165d4eHhgdDQUNx7773QaDRYsGABhg4datP5WrdujeTkZOTk5GDNmjWIjIxEfHx8meFpwYIFmDdvnq0lE5GLuZCtw6Of70badS2a+Kmw6okeCK3r7eyyiIgqZPMYJ41Gg3/++QdhYWEIDQ3FqlWrEBERgbNnz6Jdu3bQarUVn6QcAwcORPPmzbF8+fJS+/R6PfR6vfn73NxcBAcHc4wTkRs5n6nFuM93Iz1LhyB/Fb59oieCA9TOLouIajG7jnFq3bo1Tpw4gbCwMHTs2BHLly9HWFgYli1bhkaNGlW66BImk8kiHN1MqVRCqVRW+TGIyDlSr+Xj0c9342JOAcLqqrHqiZ5o7KdydllERFazOTjNmjULly5dAgDMmTMHgwcPxjfffAOFQoHY2FibzjV79mwMGTIEISEhuHHjBlatWoVt27Zh06ZNtpZFRC7udEYeHv18N67k6tEs0BvfPtETDTRezi6LiMgmVgens2fPomnTppgwYYJ5W3h4ONLS0nD8+HGEhISgXr16Nj341atX8fjjj+PSpUvw9fVFhw4dsGnTJtx77702nYeIXNvJKzcw7vM9uJanR6sGPvhmck8E1mHvMRG5H6vHOJUMBO/Xrx/69++Pvn37IijIuQtqch4nItcghECW1oB8fRG8lZ7wv2lyymOXcjFhxR5czy9Em0YarJzUHXV9GJqIyHXYZYzTli1bsG3bNmzbtg3ffvstCgsL0axZM/Tv3x/9+vVDv3790KBBgyoXT0TuI0dnwNrEdMTttFwOJTRAjcjeYWjbWIOpKxORrTWgfRNffD2pO/zUCidWTERUNZWaObygoAA7d+40B6m9e/fCYDDgjjvuwJEjR+xRZ5nY40TkPPEpGZi2MrHcBXhLtnUK9kPcxO7wVXFNOSJyPXa9qw4AvLy80L9/f9x1113o168ffv/9dyxfvhzHjx+vVMFE5F7iUzIQHbMXApaBqcSt26b2acbQREQ1gk0zhxcWFmL79u2YN28e+vXrBz8/P0ydOhVZWVn4+OOPcfbsWXvVSUQuIkdnwLSVicWhyYr+akkCnvv+IHJ0BrvXRkRkb1b3OPXv3x979uxB06ZN0adPH0yZMgWrVq2qlrmbiMh9rE1Mh67QWGZPU1mEAHSFRqxLSkd0RFO71kZEZG9W9zjt2LEDdevWRf/+/TFgwADce++9DE1EtYwQAnE7UyvVNjYhFZUYUklE5FKsDk7Z2dn47LPPoFarsXDhQjRu3Bjt27fHU089hTVr1iAjI8OedRKRC8jSGpCWqbW6t6mEAJCWqUW2lpfriMi9WX2pztvbG4MHD8bgwYMBADdu3MDff/+NrVu3YtGiRRg/fjxatmyJw4cP261YInKufH1Rldrn6Yvg783pCIjIfdk0OPxm3t7eCAgIQEBAAPz9/eHp6Yljx45VZ21E5GK8lZW6EdfMp4rtiYiczerfYiaTCfv378e2bduwdetWJCQkID8/H02aNEG/fv3wySefoF+/fvaslYiczF8tR2iAGudsvFwnAQgJUMNPzSkJiMi9WR2c/Pz8kJ+fj4YNG6Jfv354//330bdvXzRv3tye9RGRC5EkCWO7BWPRphM2t42KCDMvw0JE5K6sDk6LFy9Gv3790KpVK3vWQ0Qu7HJOAdYfuGBTGw8J8JLLMLKLc9e2JCKqDlYHpylTptizDiJycSlXbiDqy724mFMAX5UcNwoMFU6CWdLBtGxCOGcOJ6IaodKDw4mo9thz5joeXroTF3MK0CzQG78+fRdiortDJZdBwv/WpitRsk0llyE2ujvuaRXo+KKJiOyAt7gQUbl+O3QJz3yXjEKjCeGh/ljxeFf4eysQHKDGrtkDsC4pHbEJqUjL1JrbhASoERURhlHhQdB4saeJiGoOSbjxVL62rGZMRLaLSTiL+b8ehRDAfW0b4KNxneEll5U6TgiBbK0Befoi+Cg94aeWcyA4EbkNW/IEe5yIqBSTSWDhxuNYvv0MAOCxnqGY+2A7yDzKDkOSJMHfW8HJLYmoxmNwIiILhUUmvLjmIH5KvggAeHFQa0zv25w9SEREYHAiopvkFhgwbWUiEk5dh6eHhIWjOmBUOKcRICIqweBERACAK7kFiPxyL45fvgFvhQxLJ4TzbjgiolswOBERTl29gcgv9+FCtg71fJSIje6GO5v4OrssIiKXw+BEVIMJIZClNSBfXwRvpSf8y7jbbV9qJibH7UeOzoBm9bwRN7E7ggPUTqqYiMi1MTgR1UA5OgPWJqYjbqfl/EqhAWpE9i6eX8lXJcfGw5cx67sD0BeZ0DnED19EdkMA74wjIrotzuNEVMPEp2Rg2spE6AqNAICbf8BL+ppUChlGdQnCyj1pEAIY2KYBlozrDJWi9BxNREQ1HedxIqql4lMyEB2zt3gNuTL2l2zTFhrx9e40AMC47iF4c3g7eMq4AhMRUUX4m5KohsjRFU8lUNHCuzeTyyS8Mrg1QxMRkZX425KohlibmA5dodHq0AQARUaBdQcu2K8oIqIahsGJqAYQQiBuZ2ql2sYmpMKNhzoSETkUgxNRDZClNSAtU1vmuKbyCABpmVpkaw32KIuIqMZhcCKqAfL1RVVqn1fF9kREtQWDE1EN4K2s2g2yPlVsT0RUWzA4EdUA/mo5QgPUkCo+1IKE4kkx/dRye5RFRFTjMDgR1QCSJCGyd1il2kZFhJVahoWIiMrG4ERUQ4wKD4KX3PqZvz2k4hnER3YJsmNVREQ1C4MTUQ2hLzLCV23dWKWSDqZlE8Lhq+JlOiIiazE4EdUA1/P0GP/5HlzO0SNALYdK7gEJKDXmqWSbSi5DbHR33NMq0PHFEhG5Md5KQ+TmsvILMX7FHpy8mocGGiVWP9kL/t4KrEtKR2xCKtIyteZjQwLUiIoIw6jwIGi82NNERGQrSbjxlMG2rGZMVBPlaA14dMVuHLmYi8A6Sqx+sieaBfqY9wshkK01IE9fBB+lJ/zUcg4EJyK6hS15gj1ORG4qR2fAY1/uwZGLuajno8C3T/SwCE1A8d12/t4K+HsrnFQlEVHNwjFORG7oRoEBUTF78U96DgK8Ffhmck+0qF/H2WUREdV4DE5EbiZfX4TomH04cC4bvio5Vk7qgdYNGZqIiByBwYnIjWgLizAxdh/2p2WhjpcnVk7qgbaNOb6PiMhRGJyI3ESBwYjJcfux52wm6ig98fWkHmgf5OvssoiIahUGJyI3UGAw4omv9mPn6evwVsgQO7E7OgX7ObssIqJah8GJyMXpi4yY/k0Sdpy8BpVchpjo7ggP9Xd2WUREtRKDE5ELMxhNeGrVAWw5fhVecg98GdUN3ZsGOLssIqJai8GJyEUZjCbM/PYANh+9AoWnB1Y83g29mtd1dllERLUaJ8AkchIhBLK0BuTri+Ct9IT/TbN6FxlNeHZ1Mn4/fBkKmQc+eywcd7Ws5+SKiYiIwYnIwXJ0BqxNTEfcTst15EID1IjsHYaHOjfB/F+P4td/LkEuk7B0Qhf0bV3fiRUTEVEJrlVH5EDxKRmYtjIRukIjAODmH76SFeQ8PCQYTQKeHhI+Gd8Fg9o1dHidRES1CdeqI3JB8SkZiI7ZCwHLwFSiZJvRVPyvqX2aMzQREbkYDg4ncoAcnQHTViYWhyYr+nglAF8mnEWOzmDv0oiIyAYMTkQOsDYxHbpCo1WhCSjufdIVGrEuKd2udRERkW0YnIjsTAiBuJ2plWobm5AKNx6GSERU4zA4EdlZltaAtExtmeOayiMApGVqka3l5ToiIlfB4ERkZ/n6oiq1z6tieyIiqj4MTkR25q2s2s2rPlVsT0RE1YfBicjO/NVyhAaozfM0WUtC8aSYfmq5PcoiIqJKYHAisjNJkhDZO8zmMU4AEBURZl6GhYiInI/XAIjsTAgBbaFt45Q8JMBLLsPILkF2qoqIiCqDwYnIjgqLTJjz82F8u/c8gH+XVZHKnwSzpINp2YRw+Kp4mY6IyJXwUh2RnWRrCxH55V58u/c8PCTg9QfaIia6G1RyGSSg1Jinkm0quQyx0d1xT6tAxxdNRETlYo8TkR2cycjDpLj9OHstH94KGZY82hn972gAANg1ewDWJaUjNiEVaZlac5uQADWiIsIwKjwIGi/2NBERuSJJuPG0xLasZkzkKAmnrmHaykTkFhShiZ8KX0R1xR0NS38+hRDI1hqQpy+Cj9ITfmo5B4ITETmBLXmCPU5E1WjVnnN446fDKDIJdAnxw2ePd0U9H2WZx0qSBH9vBfy9FQ6ukoiIKsupY5wWLFiAbt26oU6dOqhfvz5GjBiBEydOOLMkokoxmgTm/XIEr64/hCKTwIhOjbHqiZ63DU1EROSenBqc4uPjMWPGDOzevRubN2+GwWDAfffdh/z8fGeWRWSTGwUGTI7bh5iEVADA8/e2wvtjO8FLLnNuYUREVO1caoxTRkYG6tevj/j4eNxzzz0VHs8xTuRs5zO1mBy3Hyeu3ICX3APvju6EoR0aObssIiKygduOccrJyQEABAQEOLkSooolpmXiya8ScT2/EPXrKLEisis6BPk5uywiIrIjlwlOJpMJzzzzDCIiInDnnXeWeYxer4derzd/n5ub66jyqJYRQiBLa0C+vgjeSk/433LH2/oD6Xh5zSEUGk1o11iDFZFd0chX5cSKiYjIEVwmOM2YMQOHDx/G33//fdtjFixYgHnz5jmwKqptcnQGrE1MR9xOyzmWQgPUiOwdhpGdm2DF32fx8dZTAID72jbAB490glrhMj9KRERkRy4xxumpp57CTz/9hO3bt6Np06a3Pa6sHqfg4GCOcaJqEZ+SgWkrE6ErNAKAxaK8JX1NHh4SjKbiPdP6NseL97WGhwfnXiIicmduM8ZJCIGnn34a69evx7Zt28oNTQCgVCqhVPL2bqp+8SkZiI7ZCwHLwFSiZFtJaHri7qZ4efAdjiqPiIhchFOnI5gxYwZWrlyJVatWoU6dOrh8+TIuX74MnU7nzLKolsnRGTBtZWJxaLKi/1UC8M2ec8jRGexdGhERuRinBqelS5ciJycHffv2RaNGjcxfq1evdmZZVMusTUyHrtBoVWgCinufdIVGrEtKt2tdRETkepx+qY7ImYQQiNuZWqm2sQmpiOodxvXliIhqEaf2OBE5W5bWgLRMbZnjmsojAKRlapGt5eU6IqLahMGJarV8fVGV2udVsT0REbkXBieq1byVVbta7VPF9kRE5F4YnKhW81fLERqghq2jlCQUT4rpp5bboywiInJRDE5Uq0mShJFdmtg8xgkAoiI4MJyIqLbhdQaq1VKv5WP1vvM2tfGQAC+5DCO7BNmpKiIiclXscaJa6+jFXDy8bBcu5hSgQR0lPCSgog6kkv3LJoTDV8XLdEREtQ2DE9VK+1MzMfazXbiWp0ebRhr8OvNuxER3h0ougwSUGvNUsk0llyE2ujvuaRXo+KKJiMjpeKmOap2tx69i2jeJKDCY0DXUH19EdYOvSo4+dQKxa/YArEtKR2xCKtIyteY2IQFqREWEYVR4EDRe7GkiIqqtJOHG03fbspoxEQD8lHwBz39/EEUmgX6tA/Hp+HCoFLJSxwkhkK01IE9fBB+lJ/zUcg4EJyKqoWzJE+xxolrj612peOPnIxACGN6pMf5vdEfIZWVfrZYkCf7eCvh7KxxcJRERuTIGJ6rxhBBYsuUU3tucAgB4vFco5g5rBw8P9iAREZFtGJyoRjOZBN7ccBQxCakAgJkDWuLZgS152Y2IiCqFwYlqLIPRhJfX/IN1By4AAOYMa4voiKZOroqIiNwZgxPVSAUGI55alYQ/j12FzEPC4oc7cMJKIiKqMgYnqnFyCwyYHLcfe89mQunpgU8e7YKBbRs4uywiIqoBGJzIrQghkKU1IF9fBG+lJ/xvmSbgWp4ekV/uxZGLuaij9MSKyK7o0ayuEysmIqKahMGJ3EKOzoC1iemI22k5MWVogBqRvYsnprxRYMDjX+zFmWv5qOutQNzE7rizia8TqyYiopqGE2CSy4tPycC0lYnQFRoBADd/YEv6mpRyD6jlMmRqDWjip8LXk7qjWaCPw2slIiL3wwkwqcaIT8lAdMxeCFgGphIl2woMJhQYTGjkq8Saab3QyFflwCqJiKi24CK/5LJydAZMW5lYHJqs7BfN1hZBreD/B4iIyD4YnMhlrU1Mh67QaHVoAoqnIViXlG6/ooiIqFZjcCKXJIRA3M7USrWNTUiFGw/dIyIiF8bgRC4pS2tAWqa2zHFN5REA0jK1yNYa7FEWERHVcgxO5JLy9UVVap9XxfZERERlYXAil+StrNoAb58qticiIioLgxO5JH+1HKEBakgVH2pBQvGkmH5quT3KIiKiWo7BiVySJEmI7B1WqbZREWEWy7AQERFVFwYnclmjwoPgJZdZfbyHBKgUMozsEmTHqoiIqDZjcCKXpfT0QHCAdTOAl3QwLZsQDl8VL9MREZF9cAQtuSSTSeD5Hw4i5UoeVHIPCAB6gwlA2WvVqeQyLJsQjntaBTq6VCIiqkUYnMglLfj9GDb8cwlymYQvIruhXRNfrEtKR2xCKtIytebjQgLUiIoIw6jwIGi82NNERET2xeBELufLv8/i8x1nAQCLH+6I3i3qAQCiI5oiqncYsrUG5OmL4KP0hJ9azoHgRETkMAxO5FJ+O3QJb244CgB4aXBrjOjcxGK/JEnw91bA31vhjPKIiKiW4+Bwchn7UjPxzOpkCAE81jMU0/o0d3ZJREREFhicyCWcupqHyXH7UVhkwr1tG2Dug+14CY6IiFwOgxM53dUbBYj8ci9ydAZ0CvbDR490hsyDoYmIiFwPgxM5VZ6+CNEx+3AhW4ewump8EdkVKoX1k14SERE5EoMTOY3BaML0b5Jw5GIu6norEDexO+r6KJ1dFhER0W0xOJFTCCHw6rpD2J6SAZVchi+juiG0rrezyyIiIioXgxM5xQd/nsQPienwkICPH+2MjsF+zi6JiIioQgxO5HCr953Dh3+dBAC8OeJODGjTwMkVERERWYfBiRxq6/GreHX9YQDAU/1aYHyPUCdXREREZD0GJ3KYf9KzMf2bJBhNAiO7NMHz97VydklEREQ2YXAihzh3XYuJsfugMxhxd8t6eGdkB05wSUREbodr1VG1EUIgS2tAvr4I3kpP+P+7AG9WfiGiYvbiWl4h2jTS4NPxXaDwZGYnIiL3w+BEVZajM2BtYjridqYiLVNr3h4aoMb4niHYcOgSzlzLRxM/FWKju6GOl9yJ1RIREVUegxNVSXxKBqatTISu0Fhq37lMLf7723EAgFrugdjobmig8XJ0iURERNWG10uo0uJTMhAdsxc6gxECgLhl/83fFxSZcDGnwIHVERERVT8GJ6qUHJ0B01YmFgemWxNTGQSAaSsTkaMz2Ls0IiIiu2FwokpZm5gOXaHRqtAEFIcrXaER65LS7VsYERGRHTE4kc2EEIjbmVqptrEJqRDWpi0iIiIXw+BENsvSGpCWqS01pqkiAkBaphbZWl6uIyIi98TgRDbL1xdVqX1eFdsTERE5C4MT2cxbWbVZLHyq2J6IiMhZGJzIZv5qOUID1LB1wRQJxZNi+qk5ASYREbknBieymSRJiOwdavMYJwCIigjjGnVEROS2eM2EbFZkNOFQeq5NbTwkwEsuw8guQXaqioiIyP4YnMgmBQYjnv72ADYfvVJ8qe7fzqPyZhgo6WBaNiEcvipepiMiIvfF4ERWy9EZ8ETcfuxNzYTC0wMfPdIZKoXMYq26m/NTyQU5lVyGZRPCcU+rQIfXTEREVJ0YnMgqV3ILEPnlXhy/fAN1vDyx4vGu6NGsLgBg1+wBWJeUjtiEVKRlas1tQgLUiIoIw6jwIGi82NNERETuTxJuPI1zbm4ufH19kZOTA41G4+xyaqwzGXl47Iu9uJCtQ2AdJb6a2B1tGpV+vYUQyNYakKcvgo/SE35qOQeCExGRy7MlT7DHicp18Hw2omP3ITO/EE3reeOrid0RHKAu81hJkuDvrYC/t8LBVRIRETkGgxPd1vaUDExdmQhtoRHtm/giJrob6vkonV0WERGR0zA4UZl+Sr6A578/iCKTwF0t6mHZY+Gc8ZuIiGo9p06AuX37dgwbNgyNGzeGJEn48ccfnVkO/evLv89i1nfJKDIJDOvYGF9GdWNoIiIigpODU35+Pjp27IhPPvnEmWXQv4QQWLjxOOb/ehQAENU7DB+O7QSFJyeYJyIiApx8qW7IkCEYMmSIM0ugfxUZTXh1/SF8vz8dAPDioNaY3rc574ojIiK6Ca+/1AJCCGRpDcjXF8Fb6Qn/W6YJ0BUa8fS3Sfjz2FV4SMCCke0xtluIEysmIiJyTW4VnPR6PfR6vfn73Fzb1kurbXJ0BqxNTEfcTsuJKUMD1IjsXTwxJQQwKW4f9qdlQenpgY8f7YJ72zZwYtVERESuy62C04IFCzBv3jxnl+EW4lMyLJZCudm5TC3e/PUoFm86gQBvOS5kF0Dj5YkVkd3QvWmAE6olIiJyD2416nf27NnIyckxf50/f97ZJbmk+JQMRMfshc5ghIDl+nH493sBQGcw4kJ2AfzUcnw/tRdDExERUQXcqsdJqVRCqeQEjOXJ0RkwbWVicTiycjEdvcGIRr4qu9ZFRERUEzi1xykvLw/JyclITk4GAJw9exbJyck4d+6cM8tya2sT06ErNFodmgCgwGDCuqR0+xVFRERUQzg1OO3fvx+dO3dG586dAQDPPfccOnfujDfeeMOZZbktIQTidqZWqm1sQirceL1nIiIih3Dqpbq+ffvyj3U1ytIaLO6es5YAkJapRbbWwAV6iYiIyuFWg8OpfPn6oiq1z6tieyIiopqOwakG8a7ienJcj46IiKh8DE41iL9ajtAANWxdJEVC8aSYfmq5PcoiIiKqMRicahBJkhDZO6xSbaMiwrguHRERUQUYnGqYQe0awJb84yEBKoUMI7sE2a8oIiKiGoLBqQa5nqfH1JVJMP17o2JF+akkYC2bEA5fFS/TERERVYTBqYa4mK3DmOW7cOhCDup6KzDvwXZQKWSQUDpAlWxTyWWIje6Oe1oFOr5gIiIiN8TbqGqAs9fyMWHFHlzI1qGxrxe+ntwDzQN9MKJzE6xLSkdsQqrF/E4hAWpERYRhVHgQNF7saSIiIrKWJNx4Bsrc3Fz4+voiJycHGo3G2eU4xZGLOYj8ci+u5RWiWT1vfD25B5r4Wa47J4RAttaAPH0RfJSe8FPLORCciIjoX7bkCfY4ubH9qZmIjt2HGwVFaNdYg7iJ3VHPp/QiyJIkwd9bwVnBiYiIqojByU1tO3EVU1cmosBgQrcwf3wR1Y2X3YiIiOyMwckN/XLwIp77PhkGo0Df1oFYOj4cKoXM2WURERHVeAxObubbvefw6vpDEAIY1rEx3h3dEQpP3hxJRETkCAxObmRZ/Gm88/txAMD4HiGYP/xOyDw4yJuIiMhRGJzcgBACCzeewLL40wCA6X2b48VBrXlnHBERkYMxOLk4o0ng9Z8OY9WecwCA2UPuwJQ+zZ1cFRERUe3E4ORkQghkaQ3I1xfBW+kJ/5vmWCosMuG575Px6z+XIEnAfx9qj3HdQ5xcMRERUe3F4OQkOToD1iamI26n5azeoQFqRPYOw9D2jfDyun+w7UQG5DIJH4ztjKEdGjmxYiIiIuLM4U4Qn5KBaSsToSs0AgBufgNKRi1JEmASgJfcA8sf64o+XE+OiIjILjhzuAuLT8lAdMxeCFgGphIl20ri7Av3tWZoIiIichGcAMiBcnQGTFuZWByarOjnkwC8tzkFOTqDvUsjIiIiKzA4OdDaxHToCo1WhSaguPdJV2jEuqR0u9ZFRERE1mFwchAhBOJ2plaqbWxCKtx4KBoREVGNweDkIFlaA9IytWWOayqPAJCWqUW2lpfriIiInI3ByUHy9UVVap9XxfZERERUdQxODuKtrNoNjD5VbE9ERERVx+DkIP5qOUID1LB1dTkJxZNi+qnl9iiLiIiIbMDg5CCSJKF/m/o2j3ECgKiIMC7oS0RE5AJ4/cdBvt93Hit3pdnUxkMCvOQyjOwSZKeqiIiIyBbscbKzIqMJc38+gpfW/gODSSA81B8eUvGSKuUp2b9sQjh8VbxMR0RE5AoYnOwoK78QkTF7Efvv/E3PDGyJH6b0Qkx0d6jkMkhAqTFPJdtUchlio7vjHi63QkRE5DJ4qc5OUq7cwOS4/TiXqYVaIcN7Yzpi8J2NAAB9WgVi1+wBWJeUjtiEVKRlas3tQgLUiIoIw6jwIGi82NNERETkSiThxlNS27KasSP9ceQynl2djPxCI4IDVPj88a64o2HZ9QkhkK01IE9fBB+lJ/zUcg4EJyIiciBb8gR7nKqREAIfbzmFdzenAAB6NauLT8Z3QYC34rZtJEmCv7cC/uUcQ0RERK6BwamaaAuL8OIP/2DDoUsAgMheofjPA20hl3EYGRERUU3B4FQN0rO0eOKrRBy7lAu5TML84XdiXPcQZ5dFRERE1YzBqYr2nLmO6d8k4Xp+Ier5KLB0Qji6hQU4uywiIiKyAwan2xBCIEtrQL6+CN5KT/iXMWh75e40zP35CIpMAnc20WD5Y13RxE/lpIqJiIjI3hicbpGjM2BtYjridlpOExAaoEZk7+JpAlRyGeb9cgTf7DkHABjWsTEWjeoAlULmrLKJiIjIATgdwU3iUzIwbWUidIVGALBYV66kr8lLLkNwgAopV/IgScAL97XG9L7NOYUAERGRm+J0BJUQn5KB6Ji9EECZC/GWbNMZjEi5kgcvuQc+ebQLBrRp4MAqiYiIyJl4rzyKL89NW5lYHJps6H/rykHgREREtQqDE4C1ienQFRptCk16gwnrktLtVxQRERG5nFofnIQQiPt3EV5bxSakwo2HiBEREZGNan1wytIakJapLXNcU3kEgLRMLbK1BnuURURERC6o1genfH1RldrnVbE9ERERuY9aH5y8lVW7sdCniu2JiIjIfdT64OSvliM0QA1bZ2GSUDwppp9abo+yiIiIyAXV+uAkSRIie4dVqm1URBgnviQiIqpFan1wAlC8jIpCBmszkIcEqBQyjOwSZN/CiIiIyKUwOAHwVcmxdEI4JKDC8FSyf9mEcPiqeJmOiIioNmFw+lefVoGIie4OlVxWHKBu2V+yTSWXITa6O+5pFej4IomIiMipeEvYTfq0CsSu2QOwLikdsQmpSMvUmveFBKgRFRGGUeFB0Hixp4mIiKg2koQbT31ty2rGthJCIFtrQJ6+CD5KT/ip5RwITkREVAPZkifY43QbkiTB31sBf2+Fs0shIiIiF8ExTkRERERWYnAiIiIishKDExEREZGVGJyIiIiIrMTgRERERGQlBiciIiIiKzE4EREREVmJwYmIiIjISgxORERERFZicCIiIiKyEoMTERERkZUYnIiIiIis5NaL/AohABSvakxERERUGSU5oiRXlMetg9ONGzcAAMHBwU6uhIiIiNzdjRs34OvrW+4xkrAmXrkok8mEixcvok6dOpAkydnlkB3k5uYiODgY58+fh0ajcXY5tQ5ff+fje+B8fA+cz97vgRACN27cQOPGjeHhUf4oJrfucfLw8EBQUJCzyyAH0Gg0/IXlRHz9nY/vgfPxPXA+e74HFfU0leDgcCIiIiIrMTgRERERWYnBiVyaUqnEnDlzoFQqnV1KrcTX3/n4Hjgf3wPnc6X3wK0HhxMRERE5EnuciIiIiKzE4ERERERkJQYnIiIiIisxOJHTbd++HcOGDUPjxo0hSRJ+/PFHi/1CCLzxxhto1KgRVCoVBg4ciJMnTzqn2BpqwYIF6NatG+rUqYP69etjxIgROHHihMUxBQUFmDFjBurWrQsfHx+MGjUKV65ccVLFNc/SpUvRoUMH8zw1vXr1wu+//27ez9ffsd555x1IkoRnnnnGvI3vgX3NnTsXkiRZfN1xxx3m/a7y+jM4kdPl5+ejY8eO+OSTT8rcv2jRInz00UdYtmwZ9uzZA29vbwwaNAgFBQUOrrTmio+Px4wZM7B7925s3rwZBoMB9913H/Lz883HPPvss/jll1/www8/ID4+HhcvXsTIkSOdWHXNEhQUhHfeeQeJiYnYv38/+vfvj+HDh+PIkSMA+Po70r59+7B8+XJ06NDBYjvfA/tr164dLl26ZP76+++/zftc5vUXRC4EgFi/fr35e5PJJBo2bCgWL15s3padnS2USqX49ttvnVBh7XD16lUBQMTHxwshil9zuVwufvjhB/Mxx44dEwDErl27nFVmjefv7y9WrFjB19+Bbty4IVq2bCk2b94s+vTpI2bNmiWE4M+AI8yZM0d07NixzH2u9Pqzx4lc2tmzZ3H58mUMHDjQvM3X1xc9evTArl27nFhZzZaTkwMACAgIAAAkJibCYDBYvA933HEHQkJC+D7YgdFoxHfffYf8/Hz06tWLr78DzZgxA0OHDrV4rQH+DDjKyZMn0bhxYzRr1gzjx4/HuXPnALjW6+/Wa9VRzXf58mUAQIMGDSy2N2jQwLyPqpfJZMIzzzyDiIgI3HnnnQCK3weFQgE/Pz+LY/k+VK9Dhw6hV69eKCgogI+PD9avX4+2bdsiOTmZr78DfPfdd0hKSsK+fftK7ePPgP316NEDsbGxaN26NS5duoR58+bh7rvvxuHDh13q9WdwIiILM2bMwOHDhy3GFpBjtG7dGsnJycjJycGaNWsQGRmJ+Ph4Z5dVK5w/fx6zZs3C5s2b4eXl5exyaqUhQ4aY/92hQwf06NEDoaGh+P7776FSqZxYmSVeqiOX1rBhQwAodefElStXzPuo+jz11FP49ddfsXXrVgQFBZm3N2zYEIWFhcjOzrY4nu9D9VIoFGjRogXCw8OxYMECdOzYER9++CFffwdITEzE1atX0aVLF3h6esLT0xPx8fH46KOP4OnpiQYNGvA9cDA/Pz+0atUKp06dcqmfAQYncmlNmzZFw4YN8ddff5m35ebmYs+ePejVq5cTK6tZhBB46qmnsH79emzZsgVNmza12B8eHg65XG7xPpw4cQLnzp3j+2BHJpMJer2er78DDBgwAIcOHUJycrL5q2vXrhg/frz533wPHCsvLw+nT59Go0aNXOpngJfqyOny8vJw6tQp8/dnz55FcnIyAgICEBISgmeeeQZvvfUWWrZsiaZNm+L1119H48aNMWLECOcVXcPMmDEDq1atwk8//YQ6deqYxwz4+vpCpVLB19cXkyZNwnPPPYeAgABoNBo8/fTT6NWrF3r27Onk6muG2bNnY8iQIQgJCcGNGzewatUqbNu2DZs2beLr7wB16tQxj+kr4e3tjbp165q38z2wrxdeeAHDhg1DaGgoLl68iDlz5kAmk2HcuHGu9TPg0Hv4iMqwdetWAaDUV2RkpBCieEqC119/XTRo0EAolUoxYMAAceLECecWXcOU9foDEDExMeZjdDqdmD59uvD39xdqtVo89NBD4tKlS84ruoaZOHGiCA0NFQqFQgQGBooBAwaIP/74w7yfr7/j3TwdgRB8D+xt7NixolGjRkKhUIgmTZqIsWPHilOnTpn3u8rrLwkhhGOjGhEREZF74hgnIiIiIisxOBERERFZicGJiIiIyEoMTkRERERWYnAiIiIishKDExEREZGVGJyIiIiIrMTgRERERGQlBiciF7Bt2zZIklRqAcua6NbnGhsbCz8/P6fWZI2oqCiHLfPzxRdf4L777qvSOVJTUyFJEpKTkwFU/LrPnTsXnTp1qtJj2uLo0aMICgpCfn6+wx6TqDowOBFVUUZGBqZNm4aQkBAolUo0bNgQgwYNQkJCgrNLcwtjx45FSkqKs8uo0IcffojY2Fi7P05BQQFef/11zJkzp0rnCQ4OxqVLl0qtv3Y7L7zwgsUCqvbWtm1b9OzZE++9957DHpOoOnCRX6IqGjVqFAoLCxEXF4dmzZrhypUr+Ouvv3D9+nVnl+YWVCoVVCqVs8uokK+vr0MeZ82aNdBoNIiIiKjSeWQyGRo2bGj18T4+PvDx8anSY9oqOjoaTzzxBGbPng1PT/45IvfAHieiKsjOzsaOHTuwcOFC9OvXD6GhoejevTtmz56NBx98EEDpSyYl7SRJwrZt2yzOl5CQgA4dOsDLyws9e/bE4cOHzfvS0tIwbNgw+Pv7w9vbG+3atcNvv/0G4H+XYTZs2HDb9tevX8e4cePQpEkTqNVqtG/fHt9++63F45tMJixatAgtWrSAUqlESEgI3n77bfP+8+fPY8yYMfDz80NAQACGDx+O1NTUcl+j3377Da1atYJKpUK/fv1KHX+7S0ZffvklQkJC4OPjg+nTp8NoNGLRokVo2LAh6tevb1FXyWs6efJkBAYGQqPRoH///jh48GCp83799dcICwuDr68vHnnkEdy4ccN8zJo1a9C+fXuoVCrUrVsXAwcONF9KuvVSnV6vx8yZM1G/fn14eXnhrrvuwr59+8z7S96Tv/76C127doVarUbv3r1x4sSJcl+v7777DsOGDbPYVvLY//3vf9GgQQP4+flh/vz5KCoqwosvvoiAgAAEBQUhJibG3Kasz115br1UZzKZMH/+fAQFBUGpVKJTp07YuHFjqfOvW7cO/fr1g1qtRseOHbFr1y7zMeV9ZgHg3nvvRWZmJuLj462qkcgVMDgRVUHJ/9J//PFH6PX6Kp/vxRdfxLvvvot9+/YhMDAQw4YNg8FgAADMmDEDer0e27dvx6FDh7Bw4cJSPQTltS8oKEB4eDg2bNiAw4cP48knn8Rjjz2GvXv3mtvPnj0b77zzDl5//XUcPXoUq1atQoMGDQAABoMBgwYNQp06dbBjxw4kJCTAx8cHgwcPRmFhYZnP5/z58xg5ciSGDRuG5ORkTJ48Ga+88kqFr8Pp06fx+++/Y+PGjfj222/xxRdfYOjQoUhPT0d8fDwWLlyI//znP9izZ4+5zejRo3H16lX8/vvvSExMRJcuXTBgwABkZmZanPfHH3/Er7/+il9//RXx8fF45513AACXLl3CuHHjMHHiRBw7dgzbtm3DyJEjcbt10F966SWsXbsWcXFxSEpKQosWLTBo0CCLxwOA1157De+++y72798PT09PTJw4sdzn/vfff6Nr166ltm/ZsgUXL17E9u3b8d5772HOnDl44IEH4O/vjz179mDq1KmYMmUK0tPTK3x9rfHhhx/i3Xffxf/93//hn3/+waBBg/Dggw/i5MmTpZ7fCy+8gOTkZLRq1Qrjxo1DUVERgIo/swqFAp06dcKOHTuqpWYihxBEVCVr1qwR/v7+wsvLS/Tu3VvMnj1bHDx40Lz/7NmzAoA4cOCAeVtWVpYAILZu3SqEEGLr1q0CgPjuu+/Mx1y/fl2oVCqxevVqIYQQ7du3F3Pnzi2zBmval2Xo0KHi+eefF0IIkZubK5RKpfj888/LPPbrr78WrVu3FiaTybxNr9cLlUolNm3aVGab2bNni7Zt21pse/nllwUAkZWVJYQQIiYmRvj6+pr3z5kzR6jVapGbm2veNmjQIBEWFiaMRqN5W+vWrcWCBQuEEELs2LFDaDQaUVBQYPFYzZs3F8uXL7/teV988UXRo0cPIYQQiYmJAoBITU0t87lERkaK4cOHCyGEyMvLE3K5XHzzzTfm/YWFhaJx48Zi0aJFQoj/vSd//vmn+ZgNGzYIAEKn05X5GCWfi+3bt5d67NDQ0FLP/+677zZ/X1RUJLy9vcW3334rhCj9uSupp7zXvWPHjubvGzduLN5++22LOrp16yamT59ucf4VK1aY9x85ckQAEMeOHRNClP+ZLfHQQw+JqKioco8hciXscSKqolGjRuHixYv4+eefMXjwYGzbtg1dunSp1EDiXr16mf8dEBCA1q1b49ixYwCAmTNn4q233kJERATmzJmDf/75x6b2RqMRb775Jtq3b4+AgAD4+Phg06ZNOHfuHADg2LFj0Ov1GDBgQJm1HTx4EKdOnUKdOnXMPW0BAQEoKCjA6dOny2xz7Ngx9OjR47Y13k5YWBjq1Klj/r5BgwZo27YtPDw8LLZdvXrVXFteXh7q1q1rrs3Hxwdnz561qO3W8zZq1Mh8jo4dO2LAgAFo3749Ro8ejc8//xxZWVll1nf69GkYDAaLcUhyuRzdu3c3v94lOnToYPF4AMyPeSudTgcA8PLyKrWvXbt2pZ5/+/btzd/LZDLUrVv3tue2RW5uLi5evFhqnFVERIRNz8+az6xKpYJWq61yzUSOwuBEVA28vLxw77334vXXX8fOnTsRFRVlviuq5I+duOmST8nlM1tMnjwZZ86cwWOPPYZDhw6ha9euWLJkidXtFy9ejA8//BAvv/wytm7diuTkZAwaNMh8ma2iAdp5eXkIDw9HcnKyxVdKSgoeffRRm59PeeRyucX3kiSVuc1kMplra9SoUanaTpw4gRdffLHc85acQyaTYfPmzfj999/Rtm1bLFmyBK1bt8bZs2er7blIkgQA5se8Vd26dSFJUpmBzdbXxFHKe37WfGYzMzMRGBjouIKJqojBicgO2rZtax5UXPJH4dKlS+b9txuwu3v3bvO/s7KykJKSgjZt2pi3BQcHY+rUqVi3bh2ef/55fP7551a3T0hIwPDhwzFhwgR07NgRzZo1s5gGoGXLllCpVLe9Jb1Lly44efIk6tevjxYtWlh83e6OszZt2liMobq1xurSpUsXXL58GZ6enqVqq1evntXnkSQJERERmDdvHg4cOACFQoH169eXOq558+ZQKBQWU04YDAbs27cPbdu2rfTzUCgUaNu2LY4ePVrpc1QHjUaDxo0bl5pSIyEhwebnV9Fn9vDhw+jcuXOVayZyFAYnoiq4fv06+vfvj5UrV+Kff/7B2bNn8cMPP2DRokUYPnw4gOKenJ49e+Kdd97BsWPHEB8fj//85z9lnm/+/Pn466+/cPjwYURFRaFevXrmO7meeeYZbNq0CWfPnkVSUhK2bt1qEaoqat+yZUts3rwZO3fuxLFjxzBlyhRcuXLF3NbLywsvv/wyXnrpJXz11Vc4ffo0du/ejS+++AIAMH78eNSrVw/Dhw/Hjh07cPbsWWzbtg0zZ8687YDkqVOn4uTJk3jxxRdx4sQJrFq1yi5zIQ0cOBC9evXCiBEj8McffyA1NRU7d+7Ea6+9hv3791t1jj179uC///0v9u/fj3PnzmHdunXIyMgo9RoDgLe3N6ZNm4YXX3wRGzduxNGjR/HEE09Aq9Vi0qRJVXougwYNwt9//12lc1SHF198EQsXLsTq1atx4sQJvPLKK0hOTsasWbOsPkdFn9nU1FRcuHABAwcOtMdTILILTpxBVAU+Pj7o0aMH3n//ffO4l+DgYDzxxBN49dVXzcd9+eWXmDRpEsLDw9G6dWssWrSozJmh33nnHcyaNQsnT55Ep06d8Msvv0ChUAAoHqM0Y8YMpKenQ6PRYPDgwXj//fetbv+f//wHZ86cwaBBg6BWq/Hkk09ixIgRyMnJMbd//fXX4enpiTfeeAMXL15Eo0aNMHXqVACAWq3G9u3b8fLLL2PkyJG4ceMGmjRpggEDBkCj0ZT5+oSEhGDt2rV49tlnsWTJEnTv3h3//e9/K7yzzFaSJOG3337Da6+9hujoaGRkZKBhw4a45557zHcFVkSj0WD79u344IMPkJubi9DQULz77rsYMmRImce/8847MJlMeOyxx3Djxg107doVmzZtgr+/f5Wey6RJk9C1a1fk5OQ4bO6ossycORM5OTl4/vnncfXqVbRt2xY///wzWrZsafU5KvrMfvvtt7jvvvsQGhpqj6dAZBeSELe515aI3Ma2bdvQr18/ZGVlucXyJVS+0aNHo0uXLpg9e7azS7GbwsJCtGzZEqtWraryZJ9EjsRLdURELmbx4sUOn8Xb0c6dO4dXX32VoYncDnuciGoA9jgRETkGgxMRERGRlXipjoiIiMhKDE5EREREVmJwIiIiIrISgxMRERGRlRiciIiIiKzE4ERERERkJQYnIiIiIisxOBERERFZicGJiIiIyEr/D6xO0TFTBFeOAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
diff --git a/docs/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb b/docs/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb
index 58193de..1d00e7f 100644
--- a/docs/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb
+++ b/docs/how_tos/project_pauli_operators_onto_hilbert_subspaces.ipynb
@@ -9,63 +9,67 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "74c16c91-cc5c-46ce-aff8-17d0e71ac50f",
+   "cell_type": "markdown",
+   "id": "c961d48a",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli_string, sort_and_remove_duplicates\n",
-    "\n",
-    "L = 22\n",
-    "\n",
-    "# Write all of the Pauli strings for the Heisenberg model\n",
-    "paulis = []\n",
-    "for i in range(L):\n",
-    "    pstr = [\"I\" for i in range(L)]\n",
-    "    # Sigma_x\n",
-    "    pstr[i] = \"X\"\n",
-    "    pstr[(i + 1) % L] = \"X\"\n",
-    "    paulis.append(pstr)\n",
-    "\n",
-    "    pstr = [\"I\" for i in range(L)]\n",
-    "    # Sigma_y\n",
-    "    pstr[i] = \"Y\"\n",
-    "    pstr[(i + 1) % L] = \"Y\"\n",
-    "    paulis.append(pstr)\n",
-    "\n",
-    "    pstr = [\"I\" for i in range(L)]\n",
-    "    # Sigma_z\n",
-    "    pstr[i] = \"Z\"\n",
-    "    pstr[(i + 1) % L] = \"Z\"\n",
-    "    paulis.append(pstr)"
+    "We show different ways of projecting a weighted linear combination of pauli strings\n",
+    "into a subspace defined by a subset of size $d$ computational basis states. The \n",
+    "projected operator is stored as a $d \\times d$ ``scipy.sparse.coo_matrix``.\n",
+    "\n",
+    "As an example we consider the Hamiltonian of the 1D Heisenberg model with periodic\n",
+    "boundary conditions and $L = 22$ spins:\n",
+    "$$\n",
+    "H = \\sum_{\\langle i, j \\rangle}\\left( \\sigma^x_i\\sigma^x_j + \\sigma^y_i\\sigma^y_j + \\sigma^z_i\\sigma^z_j \\right).\n",
+    "$$\n",
+    "\n",
+    "This package provides two tools to perform this projection:\n",
+    "\n",
+    "- ``qiskit_addon_sqd.qubit.matrix_elements_from_pauli()``: is a lower-level function\n",
+    "that returns the non-zero matrix elements of a Pauli string and the corresponding addresses\n",
+    "of the non-zero elements.\n",
+    "\n",
+    "- ``qiskit_addon_sqd.qubit.project_operator_to_subspace()``: is a higher-level function that \n",
+    "directly returns a ``scipy.sparse`` matrix.\n",
+    "\n",
+    "This notebook shows how to use these two tools to produce the same sparse operator."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0540e60f",
+   "id": "6c87d5e5",
    "metadata": {},
    "source": [
-    "Let's make some random bitstrings"
+    "### Subspace definition\n",
+    "\n",
+    "For this example we just generate length-22 random bitstrings. For the projection\n",
+    "functions to work as expected, it is essential that the bitstrings that define\n",
+    "the subspace are unique and sorted according to their base-10 representation.\n",
+    "\n",
+    "This can be achieved with the ``qiskit_addon_sqd.qubit.sort_and_remove_duplicates()``\n",
+    "function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "56350454",
+   "execution_count": 1,
+   "id": "7b94f840",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Subspace dimension: 4194276\n",
+      "Subspace dimension: 49718\n",
       "Full Hilbert space dimension: 4194304\n"
      ]
     }
    ],
    "source": [
+    "import numpy as np\n",
+    "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n",
+    "\n",
+    "num_spins = 22\n",
     "rand_seed = 22\n",
     "np.random.seed(rand_seed)\n",
     "\n",
@@ -74,62 +78,195 @@
     "    return np.round(np.random.rand(n_samples, n_qubits)).astype(\"int\").astype(\"bool\")\n",
     "\n",
     "\n",
-    "bts_matrix = random_bitstrings(50_000_000, L)\n",
+    "bitstring_matrix = random_bitstrings(50_000, num_spins)\n",
     "\n",
     "# NOTE: It is essential for the projection code to have the bitstrings sorted!\n",
-    "bts_matrix = sort_and_remove_duplicates(bts_matrix)\n",
+    "bitstring_matrix = sort_and_remove_duplicates(bitstring_matrix)\n",
     "\n",
-    "print(\"Subspace dimension: \" + str(bts_matrix.shape[0]))\n",
-    "print(\"Full Hilbert space dimension: \" + str(2**L))"
+    "print(\"Subspace dimension: \" + str(bitstring_matrix.shape[0]))\n",
+    "print(\"Full Hilbert space dimension: \" + str(2**num_spins))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5707b93c",
+   "metadata": {},
+   "source": [
+    "### First method: nonzero matrix elements and addresses."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
+   "id": "74c16c91-cc5c-46ce-aff8-17d0e71ac50f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIYY', 'IIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIYYII', 'IIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIYYIIII', 'IIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIXXIIIIII', 'IIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIXXIIIIIIII', 'IIIIIIIIIIIIYYIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIXXIIIIIIIIII', 'IIIIIIIIIIYYIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIXXIIIIIIIIIIII', 'IIIIIIIIYYIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIII', 'IIIIIIXXIIIIIIIIIIIIII', 'IIIIIIYYIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIII', 'IIIIXXIIIIIIIIIIIIIIII', 'IIIIYYIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIII', 'IIXXIIIIIIIIIIIIIIIIII', 'IIYYIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIII', 'XXIIIIIIIIIIIIIIIIIIII', 'YYIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIII', 'XIIIIIIIIIIIIIIIIIIIIX', 'YIIIIIIIIIIIIIIIIIIIIY', 'ZIIIIIIIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIYYI', 'IIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIYYIII', 'IIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIXXIIIII', 'IIIIIIIIIIIIIIIYYIIIII', 'IIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIXXIIIIIII', 'IIIIIIIIIIIIIYYIIIIIII', 'IIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIXXIIIIIIIII', 'IIIIIIIIIIIYYIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIXXIIIIIIIIIII', 'IIIIIIIIIYYIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIXXIIIIIIIIIIIII', 'IIIIIIIYYIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIII', 'IIIIIXXIIIIIIIIIIIIIII', 'IIIIIYYIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIII', 'IIIXXIIIIIIIIIIIIIIIII', 'IIIYYIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIII', 'IXXIIIIIIIIIIIIIIIIIII', 'IYYIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIII'],\n",
+      "              coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n",
+      " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n",
+      " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n",
+      " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n",
+      " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n",
+      " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n",
+      " 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\n",
+      " 1.+0.j, 1.+0.j, 1.+0.j])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.transpiler import CouplingMap\n",
+    "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n",
+    "\n",
+    "coupling_map = CouplingMap.from_ring(num_spins)\n",
+    "hamiltonian = generate_xyz_hamiltonian(coupling_map)\n",
+    "print(hamiltonian)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "id": "f31f5e40",
    "metadata": {},
    "outputs": [],
    "source": [
+    "from qiskit_addon_sqd.qubit import matrix_elements_from_pauli\n",
     "from scipy.sparse import coo_matrix\n",
-    "from scipy.sparse.linalg import eigsh\n",
     "\n",
-    "d = bts_matrix.shape[0]\n",
+    "d = bitstring_matrix.shape[0]\n",
     "\n",
-    "# The first Pauli operator\n",
-    "matrix_elements, row_coords, col_coords = matrix_elements_from_pauli_string(bts_matrix, paulis[0])\n",
+    "# Initialize the coo_matrix object\n",
+    "operator_from_matrix_elements = coo_matrix((d, d), dtype=\"complex128\")\n",
     "\n",
-    "# The complex double precision is required to match exactly Netket's results\n",
-    "# We can relax it to complex64 most likely\n",
-    "ham = coo_matrix((matrix_elements, (row_coords, col_coords)), (d, d), dtype=\"complex128\")\n",
-    "\n",
-    "# The remaining Pauli operators\n",
-    "# It will be a good idea to make this operation in parallel\n",
-    "for i in range(len(paulis) - 1):\n",
-    "    matrix_elements, row_coords, col_coords = matrix_elements_from_pauli_string(\n",
-    "        bts_matrix, paulis[i + 1]\n",
+    "for pauli in hamiltonian.paulis:\n",
+    "    matrix_elements, row_addresses, col_addresses = matrix_elements_from_pauli(\n",
+    "        bitstring_matrix, pauli\n",
     "    )\n",
-    "    ham += coo_matrix((matrix_elements, (row_coords, col_coords)), (d, d))"
+    "    operator_from_matrix_elements += coo_matrix(\n",
+    "        (matrix_elements, (row_addresses, col_addresses)), (d, d)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4479af24",
+   "metadata": {},
+   "source": [
+    "### Higher-level implementation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "3ce6e519",
+   "execution_count": 4,
+   "id": "e58763df",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-39.14735935]\n"
+      "Projecting term 1 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIXX ...\n",
+      "Projecting term 2 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIYY ...\n",
+      "Projecting term 3 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIZZ ...\n",
+      "Projecting term 4 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIXXII ...\n",
+      "Projecting term 5 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIYYII ...\n",
+      "Projecting term 6 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIZZII ...\n",
+      "Projecting term 7 out of 66: (1+0j) * IIIIIIIIIIIIIIIIXXIIII ...\n",
+      "Projecting term 8 out of 66: (1+0j) * IIIIIIIIIIIIIIIIYYIIII ...\n",
+      "Projecting term 9 out of 66: (1+0j) * IIIIIIIIIIIIIIIIZZIIII ...\n",
+      "Projecting term 10 out of 66: (1+0j) * IIIIIIIIIIIIIIXXIIIIII ...\n",
+      "Projecting term 11 out of 66: (1+0j) * IIIIIIIIIIIIIIYYIIIIII ...\n",
+      "Projecting term 12 out of 66: (1+0j) * IIIIIIIIIIIIIIZZIIIIII ...\n",
+      "Projecting term 13 out of 66: (1+0j) * IIIIIIIIIIIIXXIIIIIIII ...\n",
+      "Projecting term 14 out of 66: (1+0j) * IIIIIIIIIIIIYYIIIIIIII ...\n",
+      "Projecting term 15 out of 66: (1+0j) * IIIIIIIIIIIIZZIIIIIIII ...\n",
+      "Projecting term 16 out of 66: (1+0j) * IIIIIIIIIIXXIIIIIIIIII ...\n",
+      "Projecting term 17 out of 66: (1+0j) * IIIIIIIIIIYYIIIIIIIIII ...\n",
+      "Projecting term 18 out of 66: (1+0j) * IIIIIIIIIIZZIIIIIIIIII ...\n",
+      "Projecting term 19 out of 66: (1+0j) * IIIIIIIIXXIIIIIIIIIIII ...\n",
+      "Projecting term 20 out of 66: (1+0j) * IIIIIIIIYYIIIIIIIIIIII ...\n",
+      "Projecting term 21 out of 66: (1+0j) * IIIIIIIIZZIIIIIIIIIIII ...\n",
+      "Projecting term 22 out of 66: (1+0j) * IIIIIIXXIIIIIIIIIIIIII ...\n",
+      "Projecting term 23 out of 66: (1+0j) * IIIIIIYYIIIIIIIIIIIIII ...\n",
+      "Projecting term 24 out of 66: (1+0j) * IIIIIIZZIIIIIIIIIIIIII ...\n",
+      "Projecting term 25 out of 66: (1+0j) * IIIIXXIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 26 out of 66: (1+0j) * IIIIYYIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 27 out of 66: (1+0j) * IIIIZZIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 28 out of 66: (1+0j) * IIXXIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 29 out of 66: (1+0j) * IIYYIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 30 out of 66: (1+0j) * IIZZIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 31 out of 66: (1+0j) * XXIIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 32 out of 66: (1+0j) * YYIIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 33 out of 66: (1+0j) * ZZIIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 34 out of 66: (1+0j) * XIIIIIIIIIIIIIIIIIIIIX ...\n",
+      "Projecting term 35 out of 66: (1+0j) * YIIIIIIIIIIIIIIIIIIIIY ...\n",
+      "Projecting term 36 out of 66: (1+0j) * ZIIIIIIIIIIIIIIIIIIIIZ ...\n",
+      "Projecting term 37 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIXXI ...\n",
+      "Projecting term 38 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIYYI ...\n",
+      "Projecting term 39 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIZZI ...\n",
+      "Projecting term 40 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIXXIII ...\n",
+      "Projecting term 41 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIYYIII ...\n",
+      "Projecting term 42 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIZZIII ...\n",
+      "Projecting term 43 out of 66: (1+0j) * IIIIIIIIIIIIIIIXXIIIII ...\n",
+      "Projecting term 44 out of 66: (1+0j) * IIIIIIIIIIIIIIIYYIIIII ...\n",
+      "Projecting term 45 out of 66: (1+0j) * IIIIIIIIIIIIIIIZZIIIII ...\n",
+      "Projecting term 46 out of 66: (1+0j) * IIIIIIIIIIIIIXXIIIIIII ...\n",
+      "Projecting term 47 out of 66: (1+0j) * IIIIIIIIIIIIIYYIIIIIII ...\n",
+      "Projecting term 48 out of 66: (1+0j) * IIIIIIIIIIIIIZZIIIIIII ...\n",
+      "Projecting term 49 out of 66: (1+0j) * IIIIIIIIIIIXXIIIIIIIII ...\n",
+      "Projecting term 50 out of 66: (1+0j) * IIIIIIIIIIIYYIIIIIIIII ...\n",
+      "Projecting term 51 out of 66: (1+0j) * IIIIIIIIIIIZZIIIIIIIII ...\n",
+      "Projecting term 52 out of 66: (1+0j) * IIIIIIIIIXXIIIIIIIIIII ...\n",
+      "Projecting term 53 out of 66: (1+0j) * IIIIIIIIIYYIIIIIIIIIII ...\n",
+      "Projecting term 54 out of 66: (1+0j) * IIIIIIIIIZZIIIIIIIIIII ...\n",
+      "Projecting term 55 out of 66: (1+0j) * IIIIIIIXXIIIIIIIIIIIII ...\n",
+      "Projecting term 56 out of 66: (1+0j) * IIIIIIIYYIIIIIIIIIIIII ...\n",
+      "Projecting term 57 out of 66: (1+0j) * IIIIIIIZZIIIIIIIIIIIII ...\n",
+      "Projecting term 58 out of 66: (1+0j) * IIIIIXXIIIIIIIIIIIIIII ...\n",
+      "Projecting term 59 out of 66: (1+0j) * IIIIIYYIIIIIIIIIIIIIII ...\n",
+      "Projecting term 60 out of 66: (1+0j) * IIIIIZZIIIIIIIIIIIIIII ...\n",
+      "Projecting term 61 out of 66: (1+0j) * IIIXXIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 62 out of 66: (1+0j) * IIIYYIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 63 out of 66: (1+0j) * IIIZZIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 64 out of 66: (1+0j) * IXXIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 65 out of 66: (1+0j) * IYYIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 66 out of 66: (1+0j) * IZZIIIIIIIIIIIIIIIIIII ...\n"
      ]
     }
    ],
    "source": [
-    "# And we finally diagonalize\n",
-    "E, V = eigsh(ham, k=1, which=\"SA\")\n",
+    "from qiskit_addon_sqd.qubit import project_operator_to_subspace\n",
     "\n",
-    "print(E)"
+    "operator = project_operator_to_subspace(bitstring_matrix, hamiltonian, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fce97a2",
+   "metadata": {},
+   "source": [
+    "### Check that both implementations yield the same coo_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4bf56509",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0j\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((operator.power(2) - operator_from_matrix_elements.power(2)).sum())"
    ]
   }
  ],
diff --git a/docs/images/lucj_ansatz_zig_zag_pattern.jpg b/docs/images/lucj_ansatz_zig_zag_pattern.jpg
deleted file mode 100644
index 83c069c..0000000
Binary files a/docs/images/lucj_ansatz_zig_zag_pattern.jpg and /dev/null differ
diff --git a/docs/index.rst b/docs/index.rst
index 5b546fc..820e255 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -32,7 +32,7 @@ For more installation information refer to the `installation instructions <insta
 System sizes and computational requirements
 -------------------------------------------
 
-The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, `n_batches` of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this `tutorial <tutorials/01_getting_started_fermionic.ipynb>`_, those calls are inside a `for` loop. **It is highly recommended to perform these calls in parallel**.
+The computational cost of SQD is dominated by the eigenstate solver calls. At each step of the self-consistent configuration recovery iteration, `n_batches` of eigenstate solver calls are performed. The different calls are embarrassingly parallel. In this `tutorial <tutorials/01_chemistry_hamiltonian.ipynb>`_, those calls are inside a `for` loop. **It is highly recommended to perform these calls in parallel**.
 
 The :func:`qiskit_addon_sqd.fermion.solve_fermion` function is multithreaded and capable of handling systems with ~25 spacial orbitals and ~10 electrons with subspace dimensions of ~$10^7$, using ~10-30 cores.
 
diff --git a/docs/tutorials/01_chemistry_hamiltonian.ipynb b/docs/tutorials/01_chemistry_hamiltonian.ipynb
new file mode 100644
index 0000000..d300233
--- /dev/null
+++ b/docs/tutorials/01_chemistry_hamiltonian.ipynb
@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e",
+   "metadata": {},
+   "source": [
+    "# Improving energy estimation of a Fermionic Hamiltonian with SQD\n",
+    "\n",
+    "In this tutorial we implement a [Qiskit pattern](https://docs.quantum.ibm.com/guides/intro-to-patterns) showing how to post-process noisy quantum samples to find an approximation to the ground state of the $N_2$ molecule at equilibrium in the 6-31G basis set. We will use the sample-based quantum diagonalization (SQD) technique introduced by [Robledo-Moreno et al., 2024](https://arxiv.org/abs/2405.05068). In order to account for the effect of quantum noise, the self-configuration recovery technique is used.\n",
+    "\n",
+    "The pattern can be described in four steps:\n",
+    "\n",
+    "1. **Step 1: Map to quantum problem**\n",
+    "    - Generate an ansatz for estimating the ground state\n",
+    "2. **Step 2: Optimize the problem**\n",
+    "    - Transpile the ansatz for the backend\n",
+    "3. **Step 3: Execute experiments**\n",
+    "    - Draw samples from the ansatz using the ``Sampler`` primitive\n",
+    "4. **Step 4: Post-process results**\n",
+    "   - Self-consistent configuration recovery loop\n",
+    "       - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration.\n",
+    "       - Probabilistically create batches of subsamples from recovered bitstrings.\n",
+    "       - Project and diagonalize the molecular Hamiltonian over each sampled subspace.\n",
+    "       - Save the minimum ground state energy found across all batches and update the avg orbital occupancy.\n",
+    "\n",
+    "\n",
+    "For this example, the interacting-electron Hamiltonian takes the generic form:\n",
+    "\n",
+    "$$\n",
+    "\\hat{H} = \\sum_{ \\substack{pr\\\\\\sigma} } h_{pr} \\, \\hat{a}^\\dagger_{p\\sigma} \\hat{a}_{r\\sigma}\n",
+    "+ \n",
+    "\\sum_{ \\substack{prqs\\\\\\sigma\\tau} }\n",
+    "\\frac{(pr|qs)}{2} \\, \n",
+    "\\hat{a}^\\dagger_{p\\sigma}\n",
+    "\\hat{a}^\\dagger_{q\\tau}\n",
+    "\\hat{a}_{s\\tau}\n",
+    "\\hat{a}_{r\\sigma}\n",
+    "$$\n",
+    "\n",
+    "$\\hat{a}^\\dagger_{p\\sigma}$/$\\hat{a}_{p\\sigma}$ are the fermionic creation/annihalation operators associated to the $p$-th basis set element and the spin $\\sigma$. $h_{pr}$ and $(pr|qs)$ are the one- and two-body electronic integrals. These are loaded from an ``fcidump`` file with standard chemistry software.\n",
+    "\n",
+    "The SQD workflow with  self-consistent configuration recovery is depicted in the following diagram.\n",
+    "\n",
+    "![SQD diagram](../_static/images/sqd_diagram.png)\n",
+    "\n",
+    "SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\\mathcal{S} = \\{|x\\rangle \\}$ whose size does not increase exponentially with the size of the problem. In this scenario, the diagonalization of the Hamiltonian projected into the subspace defined by $\\mathcal{S}$:\n",
+    "$$\n",
+    "H_\\mathcal{S} = P_\\mathcal{S} H  P_\\mathcal{S} \\textrm{ with } P_\\mathcal{S} = \\sum_{x \\in \\mathcal{S}} |x \\rangle \\langle x |;\n",
+    "$$\n",
+    "yields a good approximation to the target eigenstate. The role of the quantum device is to produce samples of the members of $\\mathcal{S}$ only. First, a quantum circuit prepares the state $|\\Psi\\rangle$ in the quantum device. The Jordan-Wigner encoding is used. Consequently, members of the computational basis represent Fock states (electronic configurations/determinants). The circuit is sampled in the computational basis, yielding the set of noisy configurations $\\tilde{\\mathcal{X}}$. The configurations are represented by bitstrings. The set $\\tilde{\\mathcal{X}}$ is then passed into the classical post-processing block, where the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068) is used. In the SQD framework, the role of the quantum device is to produce a probability distribution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afeb054c",
+   "metadata": {},
+   "source": [
+    "### Step 1: Map problem to a quantum circuit\n",
+    "\n",
+    "In this tutorial, we will approximate the ground state energy of an $N_2$ molecule. First, we will specify the molecule and its properties. Next, we will create a [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz (quantum circuit) to generate samples from a quantum computer for ground state energy estimation.\n",
+    "\n",
+    "First, we will specify the molecule and its properties"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parsing ../molecules/n2_fci.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from pyscf import ao2mo, tools\n",
+    "\n",
+    "# Specify molecule properties\n",
+    "num_orbitals = 16\n",
+    "num_elec_a = num_elec_b = 5\n",
+    "open_shell = False\n",
+    "spin_sq = 0\n",
+    "\n",
+    "# Read in molecule from disk\n",
+    "mf_as = tools.fcidump.to_scf(\"../molecules/n2_fci.txt\")\n",
+    "hcore = mf_as.get_hcore()\n",
+    "eri = ao2mo.restore(1, mf_as._eri, num_orbitals)\n",
+    "nuclear_repulsion_energy = mf_as.mol.energy_nuc()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96bfe018",
+   "metadata": {},
+   "source": [
+    "Next, we will create the ansatz. The ``LUCJ`` ansatz is a parameterized quantum circuit, and we will initialize it with `t2` and `t1` amplitudes obtained from a CCSD calculation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "66270387",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "converged SCF energy = -108.867773675638\n",
+      "E(CCSD) = -109.0935188821144  E_corr = -0.2257452064762984\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwritten attributes  get_hcore get_ovlp  of <class 'pyscf.scf.hf_symm.SymAdaptedRHF'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyscf import cc\n",
+    "\n",
+    "mf_as.kernel()\n",
+    "mc = cc.CCSD(mf_as)\n",
+    "mc.kernel()\n",
+    "t1 = mc.t1\n",
+    "t2 = mc.t2"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "f4d882fa",
+   "metadata": {},
+   "source": [
+    "We will use the [ffsim](https://github.com/qiskit-community/ffsim/tree/main) package to create and initialize the ansatz with `t2` and `t1` amplitudes computed above. Since our molecule has a closed-shell Hartree-Fock state, we will use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced).\n",
+    "\n",
+    "As our target IBM hardware has a heavy-hex topology, we will adopt the _zig-zag_ pattern used in [Motta et al., 2023](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) for qubit interactions. In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles).\n",
+    "\n",
+    "![lucj_ansatz](../_static/images/lucj_ansatz_zig_zag_pattern.jpg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dd69a86c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ffsim\n",
+    "from qiskit import QuantumCircuit, QuantumRegister\n",
+    "\n",
+    "n_reps = 2\n",
+    "alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\n",
+    "alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\n",
+    "\n",
+    "ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\n",
+    "    t2=t2,\n",
+    "    t1=t1,\n",
+    "    n_reps=n_reps,\n",
+    "    interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\n",
+    ")\n",
+    "\n",
+    "nelec = (num_elec_a, num_elec_b)\n",
+    "\n",
+    "# create an empty quantum circuit\n",
+    "qubits = QuantumRegister(2 * num_orbitals, name=\"q\")\n",
+    "circuit = QuantumCircuit(qubits)\n",
+    "\n",
+    "# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\n",
+    "circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\n",
+    "\n",
+    "# apply the UCJ operator to the reference state\n",
+    "circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\n",
+    "circuit.measure_all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db11bf6d",
+   "metadata": {},
+   "source": [
+    "### Step 2: Optimize the problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0760b3f3",
+   "metadata": {},
+   "source": [
+    "Next, we will optimize our circuit for a target hardware. We need to choose the hardware device to use before optimizing our circuit. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "53a039d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n",
+    "\n",
+    "backend = FakeSherbrooke()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057ebbf6",
+   "metadata": {},
+   "source": [
+    "Next, we recommend the following steps to optimize the ansatz and make it hardware-compatible.\n",
+    "\n",
+    "- Select physical qubits (`initial_layout`) from the target harware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with less gates.\n",
+    "- Generate a staged pass manager using the [generate_preset_pass_manager](https://docs.quantum.ibm.com/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from qiskit with your choice of `backend` and `initial_layout`.\n",
+    "- Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes qiskit transpiler passes that decompose and merge orbitals resulting in fewer gates in the final circuit.\n",
+    "- Run the pass manager on your circuit. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7d554aa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gate counts (w/o pre-init passes): OrderedDict({'rz': 7403, 'sx': 6014, 'ecr': 2232, 'x': 315, 'measure': 32, 'barrier': 1})\n",
+      "Gate counts (w/ pre-init passes): OrderedDict({'rz': 4163, 'sx': 3189, 'ecr': 1262, 'x': 209, 'measure': 32, 'barrier': 1})\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\n",
+    "spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\n",
+    "initial_layout = spin_a_layout + spin_b_layout\n",
+    "\n",
+    "pass_manager = generate_preset_pass_manager(\n",
+    "    optimization_level=3, backend=backend, initial_layout=initial_layout\n",
+    ")\n",
+    "\n",
+    "# without PRE_INIT passes\n",
+    "isa_circuit = pass_manager.run(circuit)\n",
+    "print(f\"Gate counts (w/o pre-init passes): {isa_circuit.count_ops()}\")\n",
+    "\n",
+    "# with PRE_INIT passes\n",
+    "# We will use the circuit generated by this pass manager for hardware execution\n",
+    "pass_manager.pre_init = ffsim.qiskit.PRE_INIT\n",
+    "isa_circuit = pass_manager.run(circuit)\n",
+    "print(f\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cc1edef",
+   "metadata": {},
+   "source": [
+    "### Step 3: Execute experiments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbf7ef9f",
+   "metadata": {},
+   "source": [
+    "After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. As we only have one circuit, we will use Qiskit Runtime's [Job execution mode](https://docs.quantum.ibm.com/guides/execution-modes) and execute our circuit.\n",
+    "\n",
+    "**Note: We have commented out the code for running the circuit on a QPU and left it for the user's reference. Instead of running on real hardware in this guide, we will just generate random samples drawn from the uniform distribution.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3da09100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from qiskit_ibm_runtime import SamplerV2 as Sampler\n",
+    "\n",
+    "# sampler = Sampler(mode=backend)\n",
+    "# job = sampler.run([isa_circuit], shots=10_000)\n",
+    "# primitive_result = job.result()\n",
+    "# pub_result = primitive_result[0]\n",
+    "# counts = pub_result.data.meas.get_counts()\n",
+    "\n",
+    "from qiskit_addon_sqd.counts import generate_counts_uniform\n",
+    "\n",
+    "rand_seed = 42\n",
+    "counts = generate_counts_uniform(10_000, num_orbitals * 2, rand_seed=rand_seed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6df05b6e",
+   "metadata": {},
+   "source": [
+    "## Step 4: Post-process results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "851bc98e-9c08-4e78-9472-36301abc11d8",
+   "metadata": {},
+   "source": [
+    "First, we will transform the counts into a bitstring matrix and probability array for post-processing. Each row in the matrix represents one unique bitstring. Since qubits are normally indexed from the right of a bitstring, column ``0`` of the matrix represents qubit ``N``, and column ``N`` represents qubit ``0``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from qiskit_addon_sqd.counts import counts_to_arrays\n",
+    "\n",
+    "# Convert counts into bitstring and probability arrays\n",
+    "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb704101-0fe8-4d12-b572-b1d844e35a90",
+   "metadata": {},
+   "source": [
+    "### Iteratively refine the samples using configuration recovery and approximate the ground state at each iteration\n",
+    "\n",
+    "There are a few user-controlled options which are important for this technique:\n",
+    "- ``iterations``: Number of self-consistent configuration recovery iterations\n",
+    "- ``n_batches``: Number of batches of configurations used by the different calls to the eigenstate solver\n",
+    "- ``samples_per_batch``: Number of unique configurations to include in each batch\n",
+    "- ``max_davidson_cycles``: Maximum number of Davidson cycles run by each eigensolver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b72c048e-fe8e-4fc2-b28b-03138249074e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting configuration recovery iteration 0\n",
+      "Starting configuration recovery iteration 1\n",
+      "Starting configuration recovery iteration 2\n",
+      "Starting configuration recovery iteration 3\n",
+      "Starting configuration recovery iteration 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_addon_sqd.configuration_recovery import recover_configurations\n",
+    "from qiskit_addon_sqd.fermion import (\n",
+    "    bitstring_matrix_to_sorted_addresses,\n",
+    "    flip_orbital_occupancies,\n",
+    "    solve_fermion,\n",
+    ")\n",
+    "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n",
+    "\n",
+    "# SQD options\n",
+    "iterations = 5\n",
+    "\n",
+    "# Eigenstate solver options\n",
+    "n_batches = 10\n",
+    "samples_per_batch = 300\n",
+    "max_davidson_cycles = 200\n",
+    "\n",
+    "# Self-consistent configuration recovery loop\n",
+    "e_hist = np.zeros((iterations, n_batches))  # energy history\n",
+    "s_hist = np.zeros((iterations, n_batches))  # spin history\n",
+    "occupancy_hist = np.zeros((iterations, 2 * num_orbitals))\n",
+    "occupancies_bitwise = None  # orbital i corresponds to column i in bitstring matrix\n",
+    "for i in range(iterations):\n",
+    "    print(f\"Starting configuration recovery iteration {i}\")\n",
+    "    # On the first iteration, we have no orbital occupancy information from the\n",
+    "    # solver, so we just post-select from the full bitstring set based on hamming weight.\n",
+    "    if occupancies_bitwise is None:\n",
+    "        bs_mat_tmp = bitstring_matrix_full\n",
+    "        probs_arr_tmp = probs_arr_full\n",
+    "\n",
+    "    # If we have average orbital occupancy information, we use it to refine the full set of noisy configurations\n",
+    "    else:\n",
+    "        bs_mat_tmp, probs_arr_tmp = recover_configurations(\n",
+    "            bitstring_matrix_full,\n",
+    "            probs_arr_full,\n",
+    "            occupancies_bitwise,\n",
+    "            num_elec_a,\n",
+    "            num_elec_b,\n",
+    "            rand_seed=rand_seed,\n",
+    "        )\n",
+    "\n",
+    "    # Throw out configurations with incorrect particle number in either the spin-up or spin-down systems\n",
+    "    batches = postselect_and_subsample(\n",
+    "        bs_mat_tmp,\n",
+    "        probs_arr_tmp,\n",
+    "        num_elec_a,\n",
+    "        num_elec_b,\n",
+    "        samples_per_batch,\n",
+    "        n_batches,\n",
+    "        rand_seed=rand_seed,\n",
+    "    )\n",
+    "\n",
+    "    # Run eigenstate solvers in a loop. This loop should be parallelized for larger problems.\n",
+    "    e_tmp = np.zeros(n_batches)\n",
+    "    s_tmp = np.zeros(n_batches)\n",
+    "    occs_tmp = np.zeros((n_batches, 2 * num_orbitals))\n",
+    "    coeffs = []\n",
+    "    for j in range(n_batches):\n",
+    "        addresses = bitstring_matrix_to_sorted_addresses(batches[j], open_shell=open_shell)\n",
+    "        energy_sci, coeffs_sci, avg_occs, spin = solve_fermion(\n",
+    "            addresses,\n",
+    "            hcore,\n",
+    "            eri,\n",
+    "            spin_sq=spin_sq,\n",
+    "            max_davidson=max_davidson_cycles,\n",
+    "        )\n",
+    "        energy_sci += nuclear_repulsion_energy\n",
+    "        e_tmp[j] = energy_sci\n",
+    "        s_tmp[j] = spin\n",
+    "        occs_tmp[j, :num_orbitals] = avg_occs[0]\n",
+    "        occs_tmp[j, num_orbitals:] = avg_occs[1]\n",
+    "        coeffs.append(coeffs_sci)\n",
+    "\n",
+    "    # Combine batch results\n",
+    "    avg_occupancy = np.mean(occs_tmp, axis=0)\n",
+    "    # The occupancies from the solver should be flipped to match the bits in the bitstring matrix.\n",
+    "    occupancies_bitwise = flip_orbital_occupancies(avg_occupancy)\n",
+    "\n",
+    "    # Track optimization history\n",
+    "    e_hist[i, :] = e_tmp\n",
+    "    s_hist[i, :] = s_tmp\n",
+    "    occupancy_hist[i, :] = avg_occupancy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d78906b-4759-4506-9c69-85d4e67766b3",
+   "metadata": {},
+   "source": [
+    "### Visualize the results\n",
+    "\n",
+    "The first plot shows that after a couple of iterations we estimate the ground state energy within ``~200 mH``. Remember, the quantum samples in this demo were pure noise. The signal here comes from *a priori* knowledge of the electronic structure and molecular Hamiltonian.\n",
+    "\n",
+    "The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Data for energies plot\n",
+    "x1 = range(iterations)\n",
+    "n2_exact = -109.10288938\n",
+    "y1 = [np.min(energies) for energies in e_hist]\n",
+    "yt1 = [float(i) for i in range(-110, -106)]\n",
+    "\n",
+    "# Data for avg spatial orbital occupancy\n",
+    "y2 = avg_occupancy[:num_orbitals] + avg_occupancy[num_orbitals:]\n",
+    "x2 = range(len(y2))\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "\n",
+    "# Plot energies\n",
+    "axs[0].plot(x1, y1, label=\"Estimated\")\n",
+    "axs[0].set_xticks(x1)\n",
+    "axs[0].set_xticklabels(x1)\n",
+    "axs[0].set_yticks(yt1)\n",
+    "axs[0].set_yticklabels(yt1)\n",
+    "axs[0].axhline(y=n2_exact, color=\"red\", linestyle=\"--\", label=\"Exact\")\n",
+    "axs[0].set_title(\"Approximated Ground State Energy vs SQD Iterations\")\n",
+    "axs[0].set_xlabel(\"Iteration Index\", fontdict={\"fontsize\": 12})\n",
+    "axs[0].set_ylabel(\"Energy (Ha)\", fontdict={\"fontsize\": 12})\n",
+    "axs[0].legend()\n",
+    "\n",
+    "# Plot orbital occupancy\n",
+    "axs[1].bar(x2, y2, width=0.8)\n",
+    "axs[1].set_xticks(x2)\n",
+    "axs[1].set_xticklabels(x2)\n",
+    "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n",
+    "axs[1].set_xlabel(\"Orbital Index\", fontdict={\"fontsize\": 12})\n",
+    "axs[1].set_ylabel(\"Avg Occupancy\", fontdict={\"fontsize\": 12})\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorials/01_getting_started_fermionic.ipynb b/docs/tutorials/01_getting_started_fermionic.ipynb
deleted file mode 100644
index 44f6684..0000000
--- a/docs/tutorials/01_getting_started_fermionic.ipynb
+++ /dev/null
@@ -1,531 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9e40af77-7f0f-4dd6-ab0a-420cf396050e",
-   "metadata": {},
-   "source": [
-    "# Improving energy estimation of a Fermionic Hamiltonian with SQD\n",
-    "\n",
-    "In this tutorial, we will show how to use the `sqd` package to post-process quantum samples using the [self-consistent configuration recovery technique](https://arxiv.org/abs/2405.05068).\n",
-    "\n",
-    "This technique can be done iteratively by repeating 4 steps:\n",
-    "\n",
-    "- Correct the full set of bitstring samples, using *a priori* knowledge of particle number and the most updated orbital occupancy information obtained from the ground state approximations at each iteration.\n",
-    "  \n",
-    "- Probabilistically create batches of subsamples from corrected bitstrings.\n",
-    "  \n",
-    "- Project and diagonalize the molecular Hamiltonian over each sampled subspace.\n",
-    "  \n",
-    "- Save the minimum ground state energy found across all batches and update the avg orbital occupancy.\n",
-    "\n",
-    "In this tutorial we implement a [Qiskit patterns](https://docs.quantum.ibm.com/guides/intro-to-patterns) for post-processing quantum samples to find good ground state approximations:\n",
-    "1. **Map** problem to a quantum circuit\n",
-    "2. **Optimize** for target hardware\n",
-    "3. **Execute** on target hardware\n",
-    "4. **Post-process** results (using *SQD*)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afeb054c",
-   "metadata": {},
-   "source": [
-    "## Step 1: Map problem to a quantum circuit\n",
-    "\n",
-    "In this tutorial, we will approximate the ground state energy of an $N_2$ molecule. First, we will specify the molecule and its properties. Next, we will create a [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz (quantum circuit) to generate samples from a quantum computer for ground state energy estimation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6755afb-ca1e-4473-974b-ba89acc8abce",
-   "metadata": {},
-   "source": [
-    "### Specify the molecule and its properties"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "677f54ac-b4ed-47e3-b5ba-5366d3a520f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Parsing ../molecules/n2_fci.txt\n"
-     ]
-    }
-   ],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "from pyscf import ao2mo, tools\n",
-    "\n",
-    "# Specify molecule properties\n",
-    "num_orbitals = 16\n",
-    "num_elec_a = num_elec_b = 5\n",
-    "open_shell = False\n",
-    "spin_sq = 0\n",
-    "\n",
-    "# Read in molecule from disk\n",
-    "mf_as = tools.fcidump.to_scf(\"../molecules/n2_fci.txt\")\n",
-    "hcore = mf_as.get_hcore()\n",
-    "eri = ao2mo.restore(1, mf_as._eri, num_orbitals)\n",
-    "nuclear_repulsion_energy = mf_as.mol.energy_nuc()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96bfe018",
-   "metadata": {},
-   "source": [
-    "### Create the `LUCJ` Ansatz\n",
-    "\n",
-    "The `LUCJ` ansatz is a parameterized quantum circuit, and we will initialize it with `t2` and `t1` amplitudes obtained from a CCSD calculation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "66270387",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "converged SCF energy = -108.867773675638\n",
-      "E(CCSD) = -109.0935188821144  E_corr = -0.2257452064762984\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Overwritten attributes  get_hcore get_ovlp  of <class 'pyscf.scf.hf_symm.SymAdaptedRHF'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pyscf import cc\n",
-    "\n",
-    "mf_as.kernel()\n",
-    "mc = cc.CCSD(mf_as)\n",
-    "mc.kernel()\n",
-    "t1 = mc.t1\n",
-    "t2 = mc.t2"
-   ]
-  },
-  {
-   "attachments": {
-    "lucj_ansatz_zig-zag-pattern-rsz.jpg": {
-     "image/jpeg": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "f4d882fa",
-   "metadata": {},
-   "source": [
-    "We will use the [ffsim](https://github.com/qiskit-community/ffsim/tree/main) package to create and initialize the ansatz with `t2` and `t1` amplitudes computed above. Since our molecule has a closed-shell Hartree-Fock state, we will use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced).\n",
-    "\n",
-    "As our target IBM hardware has a heavy-hex topology, we will adopt the _zig-zag_ pattern used in [this paper](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) for qubit interactions. In this pattern, orbitals (represented by qubits) with the same spin are connected with a line topology (red and blue circles) where each line take a zig-zag shape due the heavy-hex connectivity of the target hardware. Again, due to the heavy-hex topology, orbitals for different spins have connections between every 4th orbital (0, 4, 8, etc.) (purple circles).\n",
-    "\n",
-    "![lucj_ansatz_zig-zag-pattern-rsz.jpg](attachment:lucj_ansatz_zig-zag-pattern-rsz.jpg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "dd69a86c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import ffsim\n",
-    "from qiskit import QuantumCircuit, QuantumRegister\n",
-    "\n",
-    "n_reps = 2\n",
-    "alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\n",
-    "alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\n",
-    "\n",
-    "ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\n",
-    "    t2=t2,\n",
-    "    t1=t1,\n",
-    "    n_reps=n_reps,\n",
-    "    interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\n",
-    ")\n",
-    "\n",
-    "nelec = (num_elec_a, num_elec_b)\n",
-    "\n",
-    "# create an empty quantum circuit\n",
-    "qubits = QuantumRegister(2 * num_orbitals, name=\"q\")\n",
-    "circuit = QuantumCircuit(qubits)\n",
-    "\n",
-    "# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\n",
-    "circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\n",
-    "\n",
-    "# apply the UCJ operator to the reference state\n",
-    "circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\n",
-    "circuit.measure_all()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db11bf6d",
-   "metadata": {},
-   "source": [
-    "## Step 2: Optimize for target hardware"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0760b3f3",
-   "metadata": {},
-   "source": [
-    "Next, we will optimize our circuit for a target hardware. We need to choose the hardware device to use before optimizing our circuit. We will use a fake 127-qubit backend from ``qiskit_ibm_runtime`` to emulate a real device."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "53a039d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n",
-    "\n",
-    "backend = FakeSherbrooke()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "057ebbf6",
-   "metadata": {},
-   "source": [
-    "Next, we recommend the following steps to optimize the ansatz and make it hardware-compatible.\n",
-    "\n",
-    "- Select physical qubits (`initial_layout`) from the target harware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with less gates.\n",
-    "- Generate a staged pass manager using the [generate_preset_pass_manager](https://docs.quantum.ibm.com/api/qiskit/transpiler_preset#generate_preset_pass_manager) function from qiskit with your choice of `backend` and `initial_layout`.\n",
-    "- Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes qiskit transpiler passes that decompose and merge orbitals resulting in fewer gates in the final circuit.\n",
-    "- Run the pass manager on your circuit. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7d554aa5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gate counts (w/o pre-init passes): OrderedDict({'rz': 7421, 'sx': 6016, 'ecr': 2240, 'x': 324, 'measure': 32, 'barrier': 1})\n",
-      "Gate counts (w/ pre-init passes): OrderedDict({'rz': 4155, 'sx': 3186, 'ecr': 1262, 'x': 210, 'measure': 32, 'barrier': 1})\n"
-     ]
-    }
-   ],
-   "source": [
-    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
-    "\n",
-    "spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\n",
-    "spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\n",
-    "initial_layout = spin_a_layout + spin_b_layout\n",
-    "\n",
-    "pass_manager = generate_preset_pass_manager(\n",
-    "    optimization_level=3, backend=backend, initial_layout=initial_layout\n",
-    ")\n",
-    "\n",
-    "# without PRE_INIT passes\n",
-    "isa_circuit = pass_manager.run(circuit)\n",
-    "print(f\"Gate counts (w/o pre-init passes): {isa_circuit.count_ops()}\")\n",
-    "\n",
-    "# with PRE_INIT passes\n",
-    "# We will use the circuit generated by this pass manager for hardware execution\n",
-    "pass_manager.pre_init = ffsim.qiskit.PRE_INIT\n",
-    "isa_circuit = pass_manager.run(circuit)\n",
-    "print(f\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0cc1edef",
-   "metadata": {},
-   "source": [
-    "## Step 3: Execute on target hardware"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cbf7ef9f",
-   "metadata": {},
-   "source": [
-    "After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. As we only have one circuit, we will use Qiskit Runtime's [Job execution mode](https://docs.quantum.ibm.com/guides/execution-modes) and execute our circuit.\n",
-    "\n",
-    "**Note: We have commented out the code for running the circuit on a QPU and left it for the user's reference. Instead of running on real hardware in this guide, we will just generate random samples drawn from the uniform distribution.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "3da09100",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from qiskit_ibm_runtime import SamplerV2 as Sampler\n",
-    "\n",
-    "# sampler = Sampler(mode=backend)\n",
-    "# job = sampler.run([isa_circuit], shots=10_000)\n",
-    "# primitive_result = job.result()\n",
-    "# pub_result = primitive_result[0]\n",
-    "# counts = pub_result.data.meas.get_counts()\n",
-    "\n",
-    "from qiskit_addon_sqd.counts import generate_counts_uniform\n",
-    "\n",
-    "rand_seed = 42\n",
-    "counts = generate_counts_uniform(10_000, num_orbitals * 2, rand_seed=rand_seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6df05b6e",
-   "metadata": {},
-   "source": [
-    "## Step 4: Post-process results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "851bc98e-9c08-4e78-9472-36301abc11d8",
-   "metadata": {},
-   "source": [
-    "### Transform the counts into a bitstring matrix and probability array for post-processing\n",
-    "\n",
-    "In order to speed up the bitwise processing required in this workflow, we use Numpy arrays to hold representations of the bitstrings and sampling frequencies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7a102a7f-aae6-4583-ab82-ae40fcb5496a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from qiskit_addon_sqd.counts import counts_to_arrays\n",
-    "\n",
-    "# Convert counts into bitstring and probability arrays\n",
-    "bitstring_matrix_full, probs_arr_full = counts_to_arrays(counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb704101-0fe8-4d12-b572-b1d844e35a90",
-   "metadata": {},
-   "source": [
-    "### Iteratively refine the samples using SQD and approximate the ground state\n",
-    "\n",
-    "There are a few user-controlled options which are important for this technique:\n",
-    "- ``iterations``: Number of self-consistent configuration recovery iterations\n",
-    "- ``n_batches``: Number of batches of configurations used by the different calls to the eigenstate solver\n",
-    "- ``samples_per_batch``: Number of unique configurations to include in each batch\n",
-    "- ``max_davidson_cycles``: Maximum number of Davidson cycles run by each eigensolver"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "b72c048e-fe8e-4fc2-b28b-03138249074e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting configuration recovery iteration 0\n",
-      "Starting configuration recovery iteration 1\n",
-      "Starting configuration recovery iteration 2\n",
-      "Starting configuration recovery iteration 3\n",
-      "Starting configuration recovery iteration 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "from qiskit_addon_sqd.configuration_recovery import recover_configurations\n",
-    "from qiskit_addon_sqd.fermion import (\n",
-    "    bitstring_matrix_to_sorted_addresses,\n",
-    "    flip_orbital_occupancies,\n",
-    "    solve_fermion,\n",
-    ")\n",
-    "from qiskit_addon_sqd.subsampling import postselect_and_subsample\n",
-    "\n",
-    "# SQD options\n",
-    "iterations = 5\n",
-    "\n",
-    "# Eigenstate solver options\n",
-    "n_batches = 10\n",
-    "samples_per_batch = 300\n",
-    "max_davidson_cycles = 200\n",
-    "\n",
-    "# Self-consistent configuration recovery loop\n",
-    "e_hist = np.zeros((iterations, n_batches))  # energy history\n",
-    "s_hist = np.zeros((iterations, n_batches))  # spin history\n",
-    "occupancy_hist = np.zeros((iterations, 2 * num_orbitals))\n",
-    "occupancies_bitwise = None  # orbital i corresponds to column i in bitstring matrix\n",
-    "for i in range(iterations):\n",
-    "    print(f\"Starting configuration recovery iteration {i}\")\n",
-    "    # On the first iteration, we have no orbital occupancy information from the\n",
-    "    # solver, so we just post-select from the full bitstring set based on hamming weight.\n",
-    "    if occupancies_bitwise is None:\n",
-    "        bs_mat_tmp = bitstring_matrix_full\n",
-    "        probs_arr_tmp = probs_arr_full\n",
-    "\n",
-    "    # In following iterations, we use both the occupancy info and the target hamming\n",
-    "    # weight to refine bitstrings.\n",
-    "    else:\n",
-    "        bs_mat_tmp, probs_arr_tmp = recover_configurations(\n",
-    "            bitstring_matrix_full,\n",
-    "            probs_arr_full,\n",
-    "            occupancies_bitwise,\n",
-    "            num_elec_a,\n",
-    "            num_elec_b,\n",
-    "            rand_seed=rand_seed,\n",
-    "        )\n",
-    "\n",
-    "    # Throw out samples with incorrect hamming weight and create batches of subsamples.\n",
-    "    batches = postselect_and_subsample(\n",
-    "        bs_mat_tmp,\n",
-    "        probs_arr_tmp,\n",
-    "        num_elec_a,\n",
-    "        num_elec_b,\n",
-    "        samples_per_batch,\n",
-    "        n_batches,\n",
-    "        rand_seed=rand_seed,\n",
-    "    )\n",
-    "\n",
-    "    # Run eigenstate solvers in a loop. This loop should be parallelized for larger problems.\n",
-    "    int_e = np.zeros(n_batches)\n",
-    "    int_s = np.zeros(n_batches)\n",
-    "    int_occs = np.zeros((n_batches, 2 * num_orbitals))\n",
-    "    cs = []\n",
-    "    for j in range(n_batches):\n",
-    "        addresses = bitstring_matrix_to_sorted_addresses(batches[j], open_shell=open_shell)\n",
-    "        energy_sci, coeffs_sci, avg_occs, spin = solve_fermion(\n",
-    "            addresses,\n",
-    "            hcore,\n",
-    "            eri,\n",
-    "            spin_sq=spin_sq,\n",
-    "            max_davidson=max_davidson_cycles,\n",
-    "        )\n",
-    "        energy_sci += nuclear_repulsion_energy\n",
-    "        int_e[j] = energy_sci\n",
-    "        int_s[j] = spin\n",
-    "        int_occs[j, :num_orbitals] = avg_occs[0]\n",
-    "        int_occs[j, num_orbitals:] = avg_occs[1]\n",
-    "        cs.append(coeffs_sci)\n",
-    "\n",
-    "    # Combine batch results\n",
-    "    avg_occupancy = np.mean(int_occs, axis=0)\n",
-    "    # The occupancies from the solver should be flipped to match the bits in the bitstring matrix.\n",
-    "    occupancies_bitwise = flip_orbital_occupancies(avg_occupancy)\n",
-    "\n",
-    "    # Track optimization history\n",
-    "    e_hist[i, :] = int_e\n",
-    "    s_hist[i, :] = int_s\n",
-    "    occupancy_hist[i, :] = avg_occupancy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d78906b-4759-4506-9c69-85d4e67766b3",
-   "metadata": {},
-   "source": [
-    "### Visualize the results\n",
-    "\n",
-    "The first plot shows that after a couple of iterations we estimate the ground state energy within ``~200 mH``. Remember, the quantum samples in this demo were pure noise. The signal here comes from *a priori* knowledge of the electronic structure and molecular Hamiltonian.\n",
-    "\n",
-    "The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "caffd888-e89c-4aa9-8bae-4d1bb723b35e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Data for energies plot\n",
-    "x1 = range(iterations)\n",
-    "n2_exact = -109.10288938\n",
-    "y1 = [np.min(energies) for energies in e_hist]\n",
-    "yt1 = [float(i) for i in range(-110, -106)]\n",
-    "\n",
-    "# Data for avg spatial orbital occupancy\n",
-    "y2 = avg_occupancy[:num_orbitals] + avg_occupancy[num_orbitals:]\n",
-    "x2 = range(len(y2))\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
-    "\n",
-    "# Plot energies\n",
-    "axs[0].plot(x1, y1, label=\"Estimated\")\n",
-    "axs[0].set_xticks(x1)\n",
-    "axs[0].set_xticklabels(x1)\n",
-    "axs[0].set_yticks(yt1)\n",
-    "axs[0].set_yticklabels(yt1)\n",
-    "axs[0].axhline(y=n2_exact, color=\"red\", linestyle=\"--\", label=\"Exact\")\n",
-    "axs[0].set_title(\"Approximated Ground State Energy vs SQD Iterations\")\n",
-    "axs[0].set_xlabel(\"Iteration Index\")\n",
-    "axs[0].set_ylabel(\"Energy (Ha)\")\n",
-    "axs[0].legend()\n",
-    "\n",
-    "# Plot orbital occupancy\n",
-    "axs[1].bar(x2, y2, width=0.8)\n",
-    "axs[1].set_xticks(x2)\n",
-    "axs[1].set_xticklabels(x2)\n",
-    "axs[1].set_title(\"Avg Occupancy per Spatial Orbital\")\n",
-    "axs[1].set_xlabel(\"Orbital Index\")\n",
-    "axs[1].set_ylabel(\"Avg Occupancy\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/tutorials/02_qubit_hamiltonian.ipynb b/docs/tutorials/02_qubit_hamiltonian.ipynb
new file mode 100644
index 0000000..1e390b5
--- /dev/null
+++ b/docs/tutorials/02_qubit_hamiltonian.ipynb
@@ -0,0 +1,472 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ground state energy estimation of a spin (qubit) Hamiltonian\n",
+    "\n",
+    "In this tutorial we implement a [Qiskit pattern](https://docs.quantum.ibm.com/guides/intro-to-patterns) showing how to post-process quantum samples to compute an approximation to the ground state of a XX-Z spin-1/2 chain and two-point correlators. We will use the sample-based quantum diagonalization (SQD) technique introduced by [Robledo-Moreno et al., 2024](https://arxiv.org/abs/2405.05068).\n",
+    "\n",
+    "While a Qiskit pattern typically involves 4 steps, the aim of this tutorial is to focus on the post-processing of the samples obtained from a quantum circuit whose support coincides with that of the ground state. Consequently, we generate a synthetic set of bitstrings to define the subspace and do not design an ansatz nor sample from a quantum circuit in this tutorial.\n",
+    "\n",
+    "The pattern we will implement is as follows:\n",
+    "\n",
+    "1. **Step 1: Map to quantum problem**\n",
+    "    - Specify a Hamiltonian as a Pauli operator\n",
+    "2. **Step 2: Optimize the problem**\n",
+    "    - N/A\n",
+    "3. **Step 3: Execute experiments**\n",
+    "    - N/A: Will generate synthetic quantum samples\n",
+    "4. **Step 4: Post-process results**\n",
+    "   - Project the Hamiltonian onto the subspace spanned by the samples\n",
+    "   - Diagonalize the Hamiltonian in the subspace to approximate the ground state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: Map to quantum problem\n",
+    "\n",
+    "The Hamiltonian of interest can be written as:\n",
+    "\n",
+    "$$\n",
+    "H = \\sum_i \\alpha_i P_i,\n",
+    "$$\n",
+    "\n",
+    "with $\\alpha_i$ being real coefficients and $P_i$ Pauli strings. A wide class\n",
+    "of many-body Hamiltonians can be written as the linear combination of polynomially-many \n",
+    "Pauli strings, including interacting-electron Hamiltonians, spin Hamiltonians, etc.\n",
+    "\n",
+    "In particular, we consider the ground state properties of the antiferromagnetic XX-Z spin-1/2 chain\n",
+    "with $L = 22$ sites:\n",
+    "$$\n",
+    "H = \\sum_{\\langle i, j \\rangle} J_{xy}\\left( \\sigma^x_i\\sigma^x_j + \\sigma^y_i\\sigma^y_j \\right) + \\sigma^z_i\\sigma^z_j.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SparsePauliOp(['IIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIYY', 'IIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIYYII', 'IIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIYYIIII', 'IIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIXXIIIIII', 'IIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIZZIIIIII', 'IIIIIIIIIIIIXXIIIIIIII', 'IIIIIIIIIIIIYYIIIIIIII', 'IIIIIIIIIIIIZZIIIIIIII', 'IIIIIIIIIIXXIIIIIIIIII', 'IIIIIIIIIIYYIIIIIIIIII', 'IIIIIIIIIIZZIIIIIIIIII', 'IIIIIIIIXXIIIIIIIIIIII', 'IIIIIIIIYYIIIIIIIIIIII', 'IIIIIIIIZZIIIIIIIIIIII', 'IIIIIIXXIIIIIIIIIIIIII', 'IIIIIIYYIIIIIIIIIIIIII', 'IIIIIIZZIIIIIIIIIIIIII', 'IIIIXXIIIIIIIIIIIIIIII', 'IIIIYYIIIIIIIIIIIIIIII', 'IIIIZZIIIIIIIIIIIIIIII', 'IIXXIIIIIIIIIIIIIIIIII', 'IIYYIIIIIIIIIIIIIIIIII', 'IIZZIIIIIIIIIIIIIIIIII', 'XXIIIIIIIIIIIIIIIIIIII', 'YYIIIIIIIIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIIIIIIIII', 'XIIIIIIIIIIIIIIIIIIIIX', 'YIIIIIIIIIIIIIIIIIIIIY', 'ZIIIIIIIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIYYI', 'IIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIYYIII', 'IIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIXXIIIII', 'IIIIIIIIIIIIIIIYYIIIII', 'IIIIIIIIIIIIIIIZZIIIII', 'IIIIIIIIIIIIIXXIIIIIII', 'IIIIIIIIIIIIIYYIIIIIII', 'IIIIIIIIIIIIIZZIIIIIII', 'IIIIIIIIIIIXXIIIIIIIII', 'IIIIIIIIIIIYYIIIIIIIII', 'IIIIIIIIIIIZZIIIIIIIII', 'IIIIIIIIIXXIIIIIIIIIII', 'IIIIIIIIIYYIIIIIIIIIII', 'IIIIIIIIIZZIIIIIIIIIII', 'IIIIIIIXXIIIIIIIIIIIII', 'IIIIIIIYYIIIIIIIIIIIII', 'IIIIIIIZZIIIIIIIIIIIII', 'IIIIIXXIIIIIIIIIIIIIII', 'IIIIIYYIIIIIIIIIIIIIII', 'IIIIIZZIIIIIIIIIIIIIII', 'IIIXXIIIIIIIIIIIIIIIII', 'IIIYYIIIIIIIIIIIIIIIII', 'IIIZZIIIIIIIIIIIIIIIII', 'IXXIIIIIIIIIIIIIIIIIII', 'IYYIIIIIIIIIIIIIIIIIII', 'IZZIIIIIIIIIIIIIIIIIII'],\n",
+      "              coeffs=[0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j,\n",
+      " 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j,\n",
+      " 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j,\n",
+      " 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j,\n",
+      " 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j,\n",
+      " 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j,\n",
+      " 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j,\n",
+      " 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j, 0.3+0.j, 1. +0.j, 0.3+0.j,\n",
+      " 0.3+0.j, 1. +0.j])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.transpiler import CouplingMap\n",
+    "from qiskit_addon_utils.problem_generators import generate_xyz_hamiltonian\n",
+    "\n",
+    "num_spins = 22\n",
+    "coupling_map = CouplingMap.from_ring(num_spins)\n",
+    "hamiltonian = generate_xyz_hamiltonian(coupling_map, coupling_constants=(0.3, 0.3, 1.0))\n",
+    "print(hamiltonian)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: Optimize problem (N/A)\n",
+    "\n",
+    "This tutorial is focused on post-processing samples taken from a QPU. Discussion on generating an ansatz and optimizing it for QPU execution is out of scope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Execute experiments\n",
+    "\n",
+    "This tutorial is focused on post-processing samples taken from a QPU. Discussion on running a specific state-preparation ansatz for a given Hamiltonian is out of scope. Since we don't have a specific circuit for which to evaluate the Hamiltonian, we will generate a set of synthetic samples which cover all possible pairs of domain walls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "bitstring_matrix = np.array([[i % 2 == 0 for i in range(num_spins)]], dtype=bool)\n",
+    "bitstring_matrix = np.concatenate((bitstring_matrix, np.roll(bitstring_matrix, 1, axis=1)))\n",
+    "for i in range(num_spins):\n",
+    "    for j in range(num_spins // 2):\n",
+    "        domain_wall = bitstring_matrix[0].copy()\n",
+    "        domain_wall[i] -= 1\n",
+    "        domain_wall[(i + 1 + j * 2) % num_spins] -= 1\n",
+    "        bitstring_matrix = np.concatenate((bitstring_matrix, np.expand_dims(domain_wall, axis=0)))\n",
+    "        domain_wall = bitstring_matrix[1].copy()\n",
+    "        domain_wall[i] -= 1\n",
+    "        domain_wall[(i + 1 + j * 2) % num_spins] -= 1\n",
+    "        bitstring_matrix = np.concatenate((bitstring_matrix, np.expand_dims(domain_wall, axis=0)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**For the projection functions to work as expected, it is essential that the bitstrings that define the subspace are unique and sorted according to their base-10 representation.**\n",
+    "\n",
+    "This can be achieved with the ``qiskit_addon_sqd.qubit.sort_and_remove_duplicates()`` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_addon_sqd.qubit import sort_and_remove_duplicates\n",
+    "\n",
+    "# NOTE: It is essential for the projection code to have the bitstrings sorted!\n",
+    "bitstring_matrix = sort_and_remove_duplicates(bitstring_matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4: Post-process the results\n",
+    "\n",
+    "Using the [scipy.sparse.linalg.eigsh](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigsh.html#eigsh) arguments, we request ``\"k\": 4`` to specify we want ``4`` eigenstates, and we set ``\"which\": \"SA\"`` to specify we want the smallest algebraic eigenstates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Projecting term 1 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIIIIIXX ...\n",
+      "Projecting term 2 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIIIIIYY ...\n",
+      "Projecting term 3 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIIZZ ...\n",
+      "Projecting term 4 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIIIXXII ...\n",
+      "Projecting term 5 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIIIYYII ...\n",
+      "Projecting term 6 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIZZII ...\n",
+      "Projecting term 7 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIXXIIII ...\n",
+      "Projecting term 8 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIYYIIII ...\n",
+      "Projecting term 9 out of 66: (1+0j) * IIIIIIIIIIIIIIIIZZIIII ...\n",
+      "Projecting term 10 out of 66: (0.3+0j) * IIIIIIIIIIIIIIXXIIIIII ...\n",
+      "Projecting term 11 out of 66: (0.3+0j) * IIIIIIIIIIIIIIYYIIIIII ...\n",
+      "Projecting term 12 out of 66: (1+0j) * IIIIIIIIIIIIIIZZIIIIII ...\n",
+      "Projecting term 13 out of 66: (0.3+0j) * IIIIIIIIIIIIXXIIIIIIII ...\n",
+      "Projecting term 14 out of 66: (0.3+0j) * IIIIIIIIIIIIYYIIIIIIII ...\n",
+      "Projecting term 15 out of 66: (1+0j) * IIIIIIIIIIIIZZIIIIIIII ...\n",
+      "Projecting term 16 out of 66: (0.3+0j) * IIIIIIIIIIXXIIIIIIIIII ...\n",
+      "Projecting term 17 out of 66: (0.3+0j) * IIIIIIIIIIYYIIIIIIIIII ...\n",
+      "Projecting term 18 out of 66: (1+0j) * IIIIIIIIIIZZIIIIIIIIII ...\n",
+      "Projecting term 19 out of 66: (0.3+0j) * IIIIIIIIXXIIIIIIIIIIII ...\n",
+      "Projecting term 20 out of 66: (0.3+0j) * IIIIIIIIYYIIIIIIIIIIII ...\n",
+      "Projecting term 21 out of 66: (1+0j) * IIIIIIIIZZIIIIIIIIIIII ...\n",
+      "Projecting term 22 out of 66: (0.3+0j) * IIIIIIXXIIIIIIIIIIIIII ...\n",
+      "Projecting term 23 out of 66: (0.3+0j) * IIIIIIYYIIIIIIIIIIIIII ...\n",
+      "Projecting term 24 out of 66: (1+0j) * IIIIIIZZIIIIIIIIIIIIII ...\n",
+      "Projecting term 25 out of 66: (0.3+0j) * IIIIXXIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 26 out of 66: (0.3+0j) * IIIIYYIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 27 out of 66: (1+0j) * IIIIZZIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 28 out of 66: (0.3+0j) * IIXXIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 29 out of 66: (0.3+0j) * IIYYIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 30 out of 66: (1+0j) * IIZZIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 31 out of 66: (0.3+0j) * XXIIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 32 out of 66: (0.3+0j) * YYIIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 33 out of 66: (1+0j) * ZZIIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 34 out of 66: (0.3+0j) * XIIIIIIIIIIIIIIIIIIIIX ...\n",
+      "Projecting term 35 out of 66: (0.3+0j) * YIIIIIIIIIIIIIIIIIIIIY ...\n",
+      "Projecting term 36 out of 66: (1+0j) * ZIIIIIIIIIIIIIIIIIIIIZ ...\n",
+      "Projecting term 37 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIIIIXXI ...\n",
+      "Projecting term 38 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIIIIYYI ...\n",
+      "Projecting term 39 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIIIZZI ...\n",
+      "Projecting term 40 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIIXXIII ...\n",
+      "Projecting term 41 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIIIYYIII ...\n",
+      "Projecting term 42 out of 66: (1+0j) * IIIIIIIIIIIIIIIIIZZIII ...\n",
+      "Projecting term 43 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIXXIIIII ...\n",
+      "Projecting term 44 out of 66: (0.3+0j) * IIIIIIIIIIIIIIIYYIIIII ...\n",
+      "Projecting term 45 out of 66: (1+0j) * IIIIIIIIIIIIIIIZZIIIII ...\n",
+      "Projecting term 46 out of 66: (0.3+0j) * IIIIIIIIIIIIIXXIIIIIII ...\n",
+      "Projecting term 47 out of 66: (0.3+0j) * IIIIIIIIIIIIIYYIIIIIII ...\n",
+      "Projecting term 48 out of 66: (1+0j) * IIIIIIIIIIIIIZZIIIIIII ...\n",
+      "Projecting term 49 out of 66: (0.3+0j) * IIIIIIIIIIIXXIIIIIIIII ...\n",
+      "Projecting term 50 out of 66: (0.3+0j) * IIIIIIIIIIIYYIIIIIIIII ...\n",
+      "Projecting term 51 out of 66: (1+0j) * IIIIIIIIIIIZZIIIIIIIII ...\n",
+      "Projecting term 52 out of 66: (0.3+0j) * IIIIIIIIIXXIIIIIIIIIII ...\n",
+      "Projecting term 53 out of 66: (0.3+0j) * IIIIIIIIIYYIIIIIIIIIII ...\n",
+      "Projecting term 54 out of 66: (1+0j) * IIIIIIIIIZZIIIIIIIIIII ...\n",
+      "Projecting term 55 out of 66: (0.3+0j) * IIIIIIIXXIIIIIIIIIIIII ...\n",
+      "Projecting term 56 out of 66: (0.3+0j) * IIIIIIIYYIIIIIIIIIIIII ...\n",
+      "Projecting term 57 out of 66: (1+0j) * IIIIIIIZZIIIIIIIIIIIII ...\n",
+      "Projecting term 58 out of 66: (0.3+0j) * IIIIIXXIIIIIIIIIIIIIII ...\n",
+      "Projecting term 59 out of 66: (0.3+0j) * IIIIIYYIIIIIIIIIIIIIII ...\n",
+      "Projecting term 60 out of 66: (1+0j) * IIIIIZZIIIIIIIIIIIIIII ...\n",
+      "Projecting term 61 out of 66: (0.3+0j) * IIIXXIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 62 out of 66: (0.3+0j) * IIIYYIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 63 out of 66: (1+0j) * IIIZZIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 64 out of 66: (0.3+0j) * IXXIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 65 out of 66: (0.3+0j) * IYYIIIIIIIIIIIIIIIIIII ...\n",
+      "Projecting term 66 out of 66: (1+0j) * IZZIIIIIIIIIIIIIIIIIII ...\n",
+      "Diagonalizing Hamiltonian in the subspace...\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_addon_sqd.qubit import solve_qubit\n",
+    "\n",
+    "scipy_kwargs = {\"k\": 4, \"which\": \"SA\"}\n",
+    "energies, eigenstates = solve_qubit(bitstring_matrix, hamiltonian, verbose=True, **scipy_kwargs)\n",
+    "\n",
+    "ground_state = eigenstates[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-23.45253539 -23.45253539 -18.         -18.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(energies)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compute spin-spin correlators\n",
+    "\n",
+    "Let's compute spin-spin correlators along the $x$, $y$ and $z$ axes:\n",
+    "$$\n",
+    "C^x(l) = \\frac{1}{L} \\sum_{i = 1}^L \\langle \\sigma^x_i \\sigma^x_{i + l} \\rangle- \n",
+    "\\langle \\sigma^x_i\\rangle \\langle \\sigma^x_{i + l} \\rangle\n",
+    "$$\n",
+    "$$\n",
+    "C^y(l) = \\frac{1}{L} \\sum_{i = 1}^L \\langle \\sigma^y_i \\sigma^y_{i + l} \\rangle-\n",
+    "\\langle \\sigma^y_i \\rangle \\langle \\sigma^y_{i + l} \\rangle\n",
+    "$$\n",
+    "$$\n",
+    "C^z(l) = \\frac{1}{L} \\sum_{i = 1}^L \\langle \\sigma^z_i \\sigma^z_{i + l} \\rangle-\n",
+    "\\langle \\sigma^z_i\\rangle \\langle \\sigma^z_{i + l} \\rangle\n",
+    "$$\n",
+    "\n",
+    "In order to compute the connected spin-spin correlators we first need to compute the \n",
+    "magnetization on each site along the three axes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "from qiskit_addon_sqd.qubit import project_operator_to_subspace\n",
+    "\n",
+    "s_x = np.zeros(num_spins)\n",
+    "s_y = np.zeros(num_spins)\n",
+    "s_z = np.zeros(num_spins)\n",
+    "\n",
+    "for i in range(num_spins):\n",
+    "    # Sigma_x\n",
+    "    pstr = [\"I\" for _ in range(num_spins)]\n",
+    "    pstr[i] = \"X\"\n",
+    "    pauli_op = SparsePauliOp(\"\".join(pstr))\n",
+    "    sparse_op = project_operator_to_subspace(bitstring_matrix, pauli_op)\n",
+    "    s_x[i] += np.real(np.conjugate(ground_state).T @ sparse_op @ ground_state)\n",
+    "\n",
+    "    # Sigma_y\n",
+    "    pstr = [\"I\" for i in range(num_spins)]\n",
+    "    pstr[i] = \"Y\"\n",
+    "    pauli_op = SparsePauliOp(\"\".join(pstr))\n",
+    "    sparse_op = project_operator_to_subspace(bitstring_matrix, pauli_op)\n",
+    "    s_y[i] += np.real(np.conjugate(ground_state).T @ sparse_op @ ground_state)\n",
+    "\n",
+    "    # Sigma_z\n",
+    "    pstr = [\"I\" for i in range(num_spins)]\n",
+    "    pstr[i] = \"Z\"\n",
+    "    pauli_op = SparsePauliOp(\"\".join(pstr))\n",
+    "    sparse_op = project_operator_to_subspace(bitstring_matrix, pauli_op)\n",
+    "    s_z[i] += np.real(np.conjugate(ground_state).T @ sparse_op @ ground_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(s_x, marker=\".\", markersize=20, label=\"x\")\n",
+    "plt.plot(s_y, marker=\".\", linestyle=\"--\", markersize=10, label=\"y\")\n",
+    "plt.plot(s_z, marker=\".\", markersize=10, label=\"z\")\n",
+    "\n",
+    "plt.legend(\n",
+    "    [r\"$\\langle\\sigma^x_i\\rangle$\", r\"$\\langle\\sigma^y_i\\rangle$\", r\"$\\langle\\sigma^z_i\\rangle$\"]\n",
+    ")\n",
+    "plt.xlabel(\"site, $i$\")\n",
+    "plt.ylabel(\"single-site magnetization\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have computed the average magnetization on each site, we can compute the \n",
+    "connected correlators as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_distance = num_spins // 2\n",
+    "\n",
+    "c_x = np.zeros(max_distance)\n",
+    "c_y = np.zeros(max_distance)\n",
+    "c_z = np.zeros(max_distance)\n",
+    "distance_counts = np.zeros(max_distance)\n",
+    "\n",
+    "for i in range(num_spins):\n",
+    "    for j in range(i + 1, num_spins):\n",
+    "        j_wrap = j % num_spins  # Connect qubits N and 0\n",
+    "        distance = min([abs(i - j), abs(i + (num_spins - j))])\n",
+    "\n",
+    "        # Sigma_x Sigma_x\n",
+    "        pstr = [\"I\" for _ in range(num_spins)]\n",
+    "        pstr[i] = \"X\"\n",
+    "        pstr[j_wrap] = \"X\"\n",
+    "        pauli_op = SparsePauliOp(\"\".join(pstr))\n",
+    "        sparse_op = project_operator_to_subspace(bitstring_matrix, pauli_op)\n",
+    "        c_x[distance - 1] += (\n",
+    "            np.real(np.conjugate(ground_state).T @ sparse_op @ ground_state) - s_x[i] * s_x[j_wrap]\n",
+    "        )\n",
+    "\n",
+    "        # Sigma_y Sigma_y\n",
+    "        pstr = [\"I\" for _ in range(num_spins)]\n",
+    "        pstr[i] = \"Y\"\n",
+    "        pstr[j_wrap] = \"Y\"\n",
+    "        pauli_op = SparsePauliOp(\"\".join(pstr))\n",
+    "        sparse_op = project_operator_to_subspace(bitstring_matrix, pauli_op)\n",
+    "        c_y[distance - 1] += (\n",
+    "            np.real(np.conjugate(ground_state).T @ sparse_op @ ground_state) - s_y[i] * s_y[j_wrap]\n",
+    "        )\n",
+    "\n",
+    "        # Sigma_z Sigma_z\n",
+    "        pstr = [\"I\" for _ in range(num_spins)]\n",
+    "        pstr[i] = \"Z\"\n",
+    "        pstr[j_wrap] = \"Z\"\n",
+    "        pauli_op = SparsePauliOp(\"\".join(pstr))\n",
+    "        sparse_op = project_operator_to_subspace(bitstring_matrix, pauli_op)\n",
+    "        c_z[distance - 1] += (\n",
+    "            np.real(np.conjugate(ground_state).T @ sparse_op @ ground_state) - s_z[i] * s_z[j_wrap]\n",
+    "        )\n",
+    "\n",
+    "        distance_counts[distance - 1] += 1\n",
+    "c_x /= distance_counts\n",
+    "c_y /= distance_counts\n",
+    "c_z /= distance_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the Z correlation shows true long range order (the correlator does not decay to 0). The X,Y correlators decay to 0 with distance between spin pairs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.arange(1, max_distance + 1), np.abs(c_x), marker=\".\", markersize=20, label=\"xx\")\n",
+    "plt.plot(\n",
+    "    np.arange(1, max_distance + 1),\n",
+    "    np.abs(c_y),\n",
+    "    marker=\".\",\n",
+    "    linestyle=\"--\",\n",
+    "    markersize=10,\n",
+    "    label=\"yy\",\n",
+    ")\n",
+    "plt.plot(np.arange(1, max_distance + 1), np.abs(c_z), marker=\".\", markersize=10, label=\"zz\")\n",
+    "\n",
+    "plt.legend([r\"$C^x(\\ell)$\", r\"$C^y(\\ell)$\", r\"$C^z(\\ell)$\"])\n",
+    "plt.xlabel(r\"site, $\\ell$\")\n",
+    "plt.ylabel(\"spin-spin correlators\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pyproject.toml b/pyproject.toml
index 84dc6c6..39294e3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,7 @@ classifiers = [
 requires-python = ">=3.10"
 
 dependencies = [
+    "qiskit>=1.2",
     "numpy>=1.26",
     "pyscf>=2.5",
     "jaxlib>=0.4.17",
@@ -61,6 +62,7 @@ lint = [
 ]
 notebook-dependencies = [
     "qiskit-addon-sqd",
+    "qiskit-addon-utils",
     "matplotlib",
     "ffsim",
     "qiskit",
diff --git a/qiskit_addon_sqd/qubit.py b/qiskit_addon_sqd/qubit.py
index 853e66d..f639349 100644
--- a/qiskit_addon_sqd/qubit.py
+++ b/qiskit_addon_sqd/qubit.py
@@ -18,21 +18,118 @@
    :toctree: ../stubs/
    :nosignatures:
 
-   matrix_elements_from_pauli_string
+   matrix_elements_from_pauli
    sort_and_remove_duplicates
 """
 
-from collections.abc import Sequence
 from typing import Any
 
 import jax.numpy as jnp
 import numpy as np
 from jax import Array, config, jit, vmap
 from numpy.typing import NDArray
+from qiskit.quantum_info import Pauli, SparsePauliOp
+from scipy.sparse import coo_matrix, spmatrix
+from scipy.sparse.linalg import eigsh
 
 config.update("jax_enable_x64", True)  # To deal with large integers
 
 
+def solve_qubit(
+    bitstring_matrix: np.ndarray,
+    hamiltonian: SparsePauliOp,
+    verbose: bool = False,
+    **scipy_kwargs,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Find the energies and eigenstates of a Hamiltonian projected into a subspace.
+
+    The subspace is defined by a collection of computational basis states which
+    are specified by the bitstrings (rows) in the ``bitstring_matrix``.
+
+    This function calls `scipy.sparse.linalg.eigsh <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigsh.html#eigsh>`_ for the diagonalization.
+
+    Args:
+        bitstring_matrix: A 2D array of ``bool`` representations of bit
+            values such that each row represents a single bitstring. This set of
+            bitstrings specifies the subspace into which the ``hamiltonian`` will be
+            projected and diagonalized.
+        hamiltonian: A Hamiltonian specified as a Pauli operator.
+        verbose: Whether to print the stage of the subroutine.
+        **scipy_kwargs: Keyword arguments to be passed to `scipy.sparse.linalg.eigsh <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigsh.html#eigsh>`_.
+
+    Returns:
+        - 1D array with the eigenvalues
+        - 2D array with the eigenvectors. Each column represents an eigenvector.
+
+    Raises:
+        ValueError: Bitstrings (rows) in ``bitstring_matrix`` must have length < ``64``.
+    """
+    if bitstring_matrix.shape[1] > 63:
+        raise ValueError("Bitstrings (rows) in bitstring_matrix must have length < 64.")
+
+    d, _ = bitstring_matrix.shape
+
+    ham_proj = project_operator_to_subspace(bitstring_matrix, hamiltonian, verbose=verbose)
+
+    if verbose:
+        print("Diagonalizing Hamiltonian in the subspace...")
+    energies, eigenstates = eigsh(ham_proj, **scipy_kwargs)
+
+    return energies, eigenstates
+
+
+def project_operator_to_subspace(
+    bitstring_matrix: np.ndarray,
+    hamiltonian: SparsePauliOp,
+    verbose: bool = False,
+) -> spmatrix:
+    """
+    Projects a Pauli operator into a subspace.
+
+    The subspace is defined by a collection of computational basis states, which
+    are specified by the bitstrings (rows) in ``bitstring_matrix``.
+
+    Args:
+        bitstring_matrix: A 2D array of ``bool`` representations of bit
+            values such that each row represents a single bitstring. This set of
+            bitstrings specifies the subspace into which the ``hamiltonian`` will be
+            projected and diagonalized.
+        hamiltonian: A Hamiltonian specified as a Pauli operator.
+        verbose: whether to print the stage of the subroutine.
+
+    Return:
+        A `scipy.sparse.coo_matrix <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html#coo-matrix>`_ representing the operator projected in the subspace.
+
+    Raises:
+        ValueError: Bitstrings (rows) in ``bitstring_matrix`` must have length < ``64``.
+    """
+    if bitstring_matrix.shape[1] > 63:
+        raise ValueError("Bitstrings (rows) in bitstring_matrix must have length < 64.")
+
+    d, _ = bitstring_matrix.shape
+    operator = coo_matrix((d, d), dtype="complex128")
+
+    for i, pauli in enumerate(hamiltonian.paulis):
+        coefficent = hamiltonian.coeffs[i]
+        if verbose:
+            (
+                print(
+                    f"Projecting term {i+1} out of {hamiltonian.size}: {coefficent} * "
+                    + "".join(pauli.to_label())
+                    + " ..."
+                )
+            )
+
+        matrix_elements, row_coords, col_coords = matrix_elements_from_pauli(
+            bitstring_matrix, pauli
+        )
+
+        operator += coefficent * coo_matrix((matrix_elements, (row_coords, col_coords)), (d, d))
+
+    return operator
+
+
 def sort_and_remove_duplicates(bitstring_matrix: np.ndarray, inplace: bool = True) -> np.ndarray:
     """
     Sort a bitstring matrix and remove duplicate entries.
@@ -57,8 +154,8 @@ def sort_and_remove_duplicates(bitstring_matrix: np.ndarray, inplace: bool = Tru
     return bitstring_matrix[indices, :]
 
 
-def matrix_elements_from_pauli_string(
-    bitstring_matrix: np.ndarray, pauli_str: Sequence[str]
+def matrix_elements_from_pauli(
+    bitstring_matrix: np.ndarray, pauli: Pauli
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
     Find the matrix elements of a Pauli operator in the subspace defined by the bitstrings.
@@ -80,9 +177,7 @@ def matrix_elements_from_pauli_string(
             The bitstrings in the matrix must be sorted according to
             their base-10 representation. Otherwise the projection will return
             wrong results.
-        pauli_str: A length-N sequence of single-qubit Pauli strings representing
-            an N-qubit Pauli operator. The Pauli term for qubit ``i`` should be
-            in ``pauli_str[i]`` (e.g. ``qiskit.quantum_info.Pauli("XYZ") = ["Z", "Y", "X"]``).
+        pauli: A Pauli operator.
 
     Returns:
         First array corresponds to the nonzero matrix elements
@@ -90,15 +185,15 @@ def matrix_elements_from_pauli_string(
         Third array corresponds to the column indices of the elements
 
     Raises:
-        ValueError: Input bit arrays must have length < ``64``.
+        ValueError: Bitstrings (rows) in ``bitstring_matrix`` must have length < ``64``.
     """
+    if bitstring_matrix.shape[1] > 63:
+        raise ValueError("Bitstrings (rows) in bitstring_matrix must have length < 64.")
+
     d, n_qubits = bitstring_matrix.shape
     row_array = np.arange(d)
 
-    if n_qubits > 63:
-        raise ValueError("Bit arrays must have length < 64.")
-
-    diag, sign, imag = _pauli_str_to_bool(pauli_str)
+    diag, sign, imag = _pauli_to_bool(pauli.to_label()[::-1])
 
     base_10_array_rows = _base_10_conversion_from_bts_matrix_vmap(bitstring_matrix)
 
@@ -119,17 +214,15 @@ def matrix_elements_from_pauli_string(
     return matrix_elements, row_array, col_array
 
 
-def _pauli_str_to_bool(
-    pauli_str: Sequence[str],
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+def _pauli_to_bool(pauli_str: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
     Transform sequences of Pauli strings into arrays.
 
-    An N-qubit Pauli string will be transformed into 3 arrays which represent
+    A Pauli operator will be transformed into 3 arrays which represent
     the diagonal terms of the Pauli operator.
 
     Args:
-        pauli_str: A sequence of single-qubit Pauli strings.
+        pauli_str: A Pauli string such that index ``0`` corresponds to qubit ``0``.
 
     Returns:
         A 3-tuple:
@@ -142,19 +235,19 @@ def _pauli_str_to_bool(
     sign = []
     imag = []
     for p in pauli_str:
-        if p == "I" or p == "i":
+        if p == "I":
             diag.append(True)
             sign.append(False)
             imag.append(False)
-        if p == "X" or p == "x":
+        if p == "X":
             diag.append(False)
             sign.append(False)
             imag.append(False)
-        if p == "Y" or p == "y":
+        if p == "Y":
             diag.append(False)
             sign.append(True)
             imag.append(True)
-        if p == "Z" or p == "z":
+        if p == "Z":
             diag.append(True)
             sign.append(True)
             imag.append(False)
@@ -163,14 +256,14 @@ def _pauli_str_to_bool(
 
 
 def _connected_elements_and_amplitudes_bool(
-    bit_array: np.ndarray, diag: np.ndarray, sign: np.ndarray, imag: np.ndarray
+    bitstring_matrix: np.ndarray, diag: np.ndarray, sign: np.ndarray, imag: np.ndarray
 ) -> tuple[NDArray[np.bool_], Array]:
     """
     Find the connected element to computational basis state |X>.
 
     Given a Pauli operator represented by ``{diag, sign, imag}``.
     Args:
-        bit_array: A 1D array of ``bool`` representations of bits.
+        bitstring_matrix: A 1D array of ``bool`` representations of bits.
         diag: ``bool`` whether the Pauli operator is diagonal. Only ``True``
             for I and Z.
         sign: ``bool`` Whether there is a change of sign in the matrix elements
@@ -182,9 +275,10 @@ def _connected_elements_and_amplitudes_bool(
         A matrix of bitstrings where each row is the connected element to the
             input the matrix element.
     """
-    bit_array_mask: NDArray[np.bool_] = bit_array == diag
-    return bit_array_mask, jnp.prod(
-        (-1) ** (jnp.logical_and(bit_array, sign)) * jnp.array(1j, dtype="complex64") ** (imag)
+    bitstring_matrix_mask: NDArray[np.bool_] = bitstring_matrix == diag
+    return bitstring_matrix_mask, jnp.prod(
+        (-1) ** (jnp.logical_and(bitstring_matrix, sign))
+        * jnp.array(1j, dtype="complex64") ** (imag)
     )