diff --git a/qiskit/ignis/verification/randomized_benchmarking/__init__.py b/qiskit/ignis/verification/randomized_benchmarking/__init__.py
index c3c313b46..3bcf90fea 100644
--- a/qiskit/ignis/verification/randomized_benchmarking/__init__.py
+++ b/qiskit/ignis/verification/randomized_benchmarking/__init__.py
@@ -21,7 +21,7 @@
 from .circuits import randomized_benchmarking_seq
 from .dihedral import (CNOTDihedral, decompose_cnotdihedral, random_cnotdihedral)
 from .fitters import (RBFitter, InterleavedRBFitter, PurityRBFitter,
-                      CNOTDihedralRBFitter)
+                      CNOTDihedralRBFitter, CorrelatedRBFitter)
 from .rb_utils import (count_gates, gates_per_clifford,
                        coherence_limit, twoQ_clifford_error,
                        calculate_1q_epg, calculate_2q_epg, calculate_1q_epc, calculate_2q_epc)
diff --git a/qiskit/ignis/verification/randomized_benchmarking/examples/corr_randomized_benchmarking.ipynb b/qiskit/ignis/verification/randomized_benchmarking/examples/corr_randomized_benchmarking.ipynb
new file mode 100644
index 000000000..4df7574e5
--- /dev/null
+++ b/qiskit/ignis/verification/randomized_benchmarking/examples/corr_randomized_benchmarking.ipynb
@@ -0,0 +1,1319 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Correlated Randomized Benchmarking\n",
+    "---\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "**Randomization benchmarking (RB)** is a well-known technique to measure average gate performance by running sequences of random Clifford gates that should return the qubits to the initial state. \n",
+    "Qiskit Ignis has tools to generate one- and two-qubit Clifford gate sequences simultaneously. If we run simultaneous RB we can look at the decay of correlated Z-Pauli's to extract the weight and locality of errors (see paper XXX).\n",
+    "\n",
+    "This notebook gives an example for how to use the ``ignis.verification.randomized_benchmarking`` module to run correlated RB. This particular example shows how to run single qubit simultaneous randomized benchmarking on 5 qubits. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import general libraries (needed for functions)\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython import display\n",
+    "\n",
+    "#Import Qiskit classes classes\n",
+    "import qiskit\n",
+    "from qiskit.providers.aer.noise import NoiseModel\n",
+    "from qiskit.providers.aer.noise.errors.standard_errors import coherent_unitary_error, thermal_relaxation_error\n",
+    "\n",
+    "#Import the RB Functions\n",
+    "import qiskit.ignis.verification.randomized_benchmarking as rb\n",
+    "\n",
+    "#Import the Fitter Functions\n",
+    "from qiskit.ignis.verification.randomized_benchmarking import CorrelatedRBFitter\n",
+    "\n",
+    "# Import measurement calibration functions\n",
+    "from qiskit.ignis.mitigation.measurement import (complete_meas_cal, tensored_meas_cal,\n",
+    "                                                 CompleteMeasFitter, TensoredMeasFitter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Select the Parameters of the RB Run\n",
+    "\n",
+    "First, wee need to choose the following parameters:\n",
+    "\n",
+    "- **nseeds:** The number of seeds. For each seed there you will get a separate list of output circuits in rb_circs.\n",
+    "- **length_vector:** The length vector of Clifford lengths. Must be in ascending order. RB sequences of increasing length grow on top of the previous sequences.\n",
+    "- **rb_pattern:** A list of the form [[i,j],[k],...] which will make simultaneous RB sequences where Qi,Qj are a 2-qubit RB sequence and Qk is a 1-qubit sequence, etc. The number of qubits is the sum of the entries. For 'regular' RB the qubit_pattern is just [[0]],[[0,1]].\n",
+    "- **length_multiplier:** If this is an array it scales each rb_sequence by the multiplier.\n",
+    "- **seed_offset:** What to start the seeds at (e.g. if we want to add more seeds later).\n",
+    "- **align_cliffs:**  If true adds a barrier across all qubits in rb_pattern after each set of cliffords.\n",
+    "\n",
+    "In this example we have 4 qubits Q0,Q1,Q2,Q3. \n",
+    "We are running 1Q RB (on each qubit) simultaneously, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##### Number of qubits. There are 4 qubits: Q0,Q1,Q2,Q3.\n",
+    "nQ = 4\n",
+    "#Number of seeds (random sequences)\n",
+    "nseeds = 20 #set to 20 for testing\n",
+    "#Number of Cliffords in the sequence (start, stop, steps)\n",
+    "nCliffs = list(np.arange(1,1000,50))\n",
+    "\n",
+    "#1Q RB on Q0-3 simultaneously\n",
+    "rb_pattern = [[0],[1],[2],[3]]\n",
+    "basis_gates = ['u1', 'u2', 'u3', 'cx']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## create the noise model\n",
+    "we will try to simulate the device noise - we will use thermal relaxation .\n",
+    "\n",
+    "and add a non local error with a matrix of ZZ error between qubits 1 and 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load the backend\n",
+    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
+    "\n",
+    "# create noise model\n",
+    "# create ZZ noise between qubits 1 and 2\n",
+    "noise_model = NoiseModel()\n",
+    "\n",
+    "zz_unitary = np.eye(4,dtype=complex)\n",
+    "zz_unitary[3,3] = np.exp(0.5j * (np.pi / 5))\n",
+    "error = coherent_unitary_error(zz_unitary)\n",
+    "\n",
+    "noise_model.add_nonlocal_quantum_error(error, basis_gates, [1], [1, 2])\n",
+    "noise_model.add_nonlocal_quantum_error(error, basis_gates, [2], [2, 1])\n",
+    "\n",
+    "# Add T1/T2 noise to the simulation\n",
+    "t_1 = 10\n",
+    "t_2 = 20\n",
+    "gate1q = 0.1\n",
+    "gate2q = 0.5\n",
+    "noise_model.add_all_qubit_quantum_error(thermal_relaxation_error(t_1, t_2, gate1q), 'u2')\n",
+    "noise_model.add_all_qubit_quantum_error(thermal_relaxation_error(t_1, t_2, 2 * gate1q), 'u3')\n",
+    "noise_model.add_all_qubit_quantum_error(\n",
+    "    thermal_relaxation_error(t_1, t_2, gate2q).tensor(thermal_relaxation_error(t_1, t_2, gate2q)), 'cx')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create and run the measurement calibration\n",
+    "Measurement correction is needed because we want to measure the correlations during RB independent of any measurement correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 2, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate the calibration circuits\n",
+    "qr = qiskit.QuantumRegister(4)\n",
+    "qubit_list = []\n",
+    "for i in rb_pattern:\n",
+    "    for j in i:\n",
+    "        qubit_list.append(j)\n",
+    "print(qubit_list)\n",
+    "meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qr, circlabel='mcal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shots = 1024\n",
+    "meas_calibs_trans = qiskit.compiler.transpile(meas_calibs, \n",
+    "                                              backend=backend, \n",
+    "                                              basis_gates=basis_gates, optimization_level=0)\n",
+    "qobj = qiskit.compiler.assemble(meas_calibs_trans, shots=shots)\n",
+    "job = backend.run(qobj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The calibration matrix without noise is the identity matrix\n",
+    "meas_fitter = CompleteMeasFitter(job.result(), state_labels, circlabel='mcal')\n",
+    "print(meas_fitter.cal_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the filter object\n",
+    "meas_filter = meas_fitter.filter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate RB sequences\n",
+    "\n",
+    "We generate RB sequences. We start with a small example (so it doesn't take too long to run).\n",
+    "\n",
+    "In order to generate the RB sequences **rb_circs**, which is a list of lists of quantum circuits, \n",
+    "we run the function **rb.randomized_benchmarking_seq**.\n",
+    "\n",
+    "This function returns:\n",
+    "\n",
+    "- **rb_circs:** A list of lists of circuits for the rb sequences (separate list for each seed).\n",
+    "- **xdata:** The Clifford lengths (with multiplier if applicable).\n",
+    "- **rb_opts_dict:** Option dictionary back out with default options appended."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rb_opts = {}\n",
+    "rb_opts['length_vector'] = nCliffs\n",
+    "rb_opts['nseeds'] = nseeds\n",
+    "rb_opts['rb_pattern'] = rb_pattern\n",
+    "rb_opts['rand_seed'] = 0\n",
+    "rb_circs, xdata = rb.randomized_benchmarking_seq(**rb_opts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, we print the circuit corresponding to the first RB sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      ┌───┐┌───┐  ░  ┌─────┐ ┌───┐      ┌─┐        \n",
+      "qr_0: ┤ Y ├┤ S ├──░──┤ SDG ├─┤ Y ├──────┤M├────────\n",
+      "      ├───┤├───┤┌───┐└──░──┘┌┴───┴┐┌───┐└╥┘┌───┐┌─┐\n",
+      "qr_1: ┤ X ├┤ H ├┤ S ├───░───┤ SDG ├┤ H ├─╫─┤ X ├┤M├\n",
+      "      ├───┤└─░─┘├───┤   ░   └─┬─┬─┘└───┘ ║ └───┘└╥┘\n",
+      "qr_2: ┤ Y ├──░──┤ Y ├─────────┤M├────────╫───────╫─\n",
+      "      ├───┤┌───┐└─░─┘ ┌───┐   └╥┘  ┌───┐ ║  ┌─┐  ║ \n",
+      "qr_3: ┤ Y ├┤ H ├──░───┤ H ├────╫───┤ Y ├─╫──┤M├──╫─\n",
+      "      └───┘└───┘  ░   └───┘    ║   └───┘ ║  └╥┘  ║ \n",
+      "cr: 4/═════════════════════════╩═════════╩═══╩═══╩═\n",
+      "                               2         0   3   1 \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(rb_circs[0][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Execute RB Circuits\n",
+    "\n",
+    "We can execute the RB sequences using IBMQ provider, \n",
+    "and obtain a list of results **result_list**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finished seed 0\n",
+      "finished seed 1\n",
+      "finished seed 2\n",
+      "finished seed 3\n",
+      "finished seed 4\n",
+      "finished seed 5\n",
+      "finished seed 6\n",
+      "finished seed 7\n",
+      "finished seed 8\n",
+      "finished seed 9\n",
+      "finished seed 10\n",
+      "finished seed 11\n",
+      "finished seed 12\n",
+      "finished seed 13\n",
+      "finished seed 14\n",
+      "finished seed 15\n",
+      "finished seed 16\n",
+      "finished seed 17\n",
+      "finished seed 18\n",
+      "finished seed 19\n",
+      "Finished Running\n"
+     ]
+    }
+   ],
+   "source": [
+    "basis_gates = ['u1','u2','u3','cx'] # use U,CX for now\n",
+    "shots = 1024\n",
+    "# run the noisy simulation (with fixed seed)\n",
+    "results = []\n",
+    "for circ_ind, circuit in enumerate(rb_circs):\n",
+    "    results.append(qiskit.execute(circuit, backend=backend,\n",
+    "                                      basis_gates=basis_gates,\n",
+    "                                      shots=shots,\n",
+    "                                      noise_model=noise_model,\n",
+    "                                      seed_simulator=42).result())   \n",
+    "    print(\"finished seed %s\" % str(circ_ind))\n",
+    "print(\"Finished Running\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get statistics about the survival probabilities\n",
+    "\n",
+    "The results in **results_list** should fit to an exponentially decaying function $A \\cdot \\alpha ^ m + B$, where $m$ is the Clifford length.\n",
+    "\n",
+    "We fit to each of the correlators."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fit the RB data to get the decay parameters \"alpha\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#correct results\n",
+    "result_corr = []\n",
+    "for result in results:\n",
+    "    result_corr.append(meas_filter.apply(result))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit = CorrelatedRBFitter(None, xdata, rb_opts['rb_pattern'])\n",
+    "\n",
+    "for seed_num, data in enumerate(result_corr):\n",
+    "    \n",
+    "    # Add another seed to the data\n",
+    "    if seed_num>-1:\n",
+    "        rbfit.add_data([data],rerun_fit=False)\n",
+    "        \n",
+    "rbfit.calc_data()\n",
+    "rbfit.calc_statistics()\n",
+    "rbfit.fit_data()\n",
+    "\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "axis = [plt.subplot(1, 2, 1), plt.subplot(1, 2, 2)]\n",
+    "    \n",
+    "for i,j in enumerate(np.arange(4,6)):\n",
+    "    pattern_ind = j\n",
+    "\n",
+    "    # Plot the essence by calling plot_rb_data\n",
+    "    rbfit.plot_rb_data(pattern_ind, ax=axis[i], add_label=True, show_plt=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'0001': 0.9936609378327881,\n",
+       " '0010': 0.9930187251679536,\n",
+       " '0011': 0.9860706316072829,\n",
+       " '0100': 0.9918314168463525,\n",
+       " '0101': 0.9869562552893651,\n",
+       " '0110': 0.9910751468969837,\n",
+       " '0111': 0.9833039092283457,\n",
+       " '1000': 0.9938722912243482,\n",
+       " '1001': 0.9861845508496924,\n",
+       " '1010': 0.9872213148146436,\n",
+       " '1011': 0.9791206600354609,\n",
+       " '1100': 0.9855576922164394,\n",
+       " '1101': 0.9796498071199026,\n",
+       " '1110': 0.9862425763645215,\n",
+       " '1111': 0.9778913960597402}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#These are the decay parameters from the fit\n",
+    "rbfit.fit_alphas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.064247215994554e-06"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rbfit.fit_alphas_to_epsilon()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'0001': [0.0068011544669276955, 0.0006465996859431455],\n",
+       " '0010': [0.0010145644670958593, 0.0006522513299894473],\n",
+       " '0011': [1.3114668588358226e-10, 0.0009202248841110362],\n",
+       " '0100': [0.0014803839672063853, 0.0006517953605761844],\n",
+       " '0101': [0.00022701904095870476, 0.0009197499483009586],\n",
+       " '0110': [0.005450921413666263, 0.000916402006327048],\n",
+       " '0111': [1.607096396015119e-08, 0.001298594583708941],\n",
+       " '1000': [0.005656115577044299, 0.0006477158704429475],\n",
+       " '1001': [1.0597853181254002e-11, 0.0009180917413141753],\n",
+       " '1010': [0.000742810265066398, 0.0009198982654985438],\n",
+       " '1011': [4.041371249592524e-10, 0.0012974799982858661],\n",
+       " '1100': [0.000318422619192666, 0.000920171614059687],\n",
+       " '1101': [4.136002577489738e-12, 0.0012972415694825937],\n",
+       " '1110': [2.1542138470117572e-09, 0.0012990698871131025],\n",
+       " '1111': [3.7061984865063936e-09, 0.0017741557909460243]}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rbfit.fit_epsilon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.99297335, 0.99280201, 0.98582596, 0.99253515, 0.98578476,\n",
+       "       0.99079164, 0.98405311, 0.99328888, 0.98630939, 0.98687226,\n",
+       "       0.97993788, 0.98618816, 0.97948094, 0.98518761, 0.97848719])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#These are the calculated decay parameters given the depolarizing probabilities \n",
+    "#that the fitter returned\n",
+    "rbfit._calc_alphas([rbfit.fit_epsilon[i][0] for i in rbfit._subsystems])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Qubits [0, 1, 2, 3] on Backend qasm_simulator')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plot the depolarizing probabilities\n",
+    "fig = plt.figure(figsize=(14, 6))\n",
+    "rbfit.plot_epsilon(ax=fig.gca())\n",
+    "fig.gca().set_title(\"Qubits %s on Backend %s\"%(str(qubit_list),backend.name()), fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the correlator '0110' has high value, like expected.\n",
+    "\n",
+    "### for comparison - run with different noise model.\n",
+    "set the ZZ error only between qubits 0 and 3.\n",
+    "\n",
+    "the rest will be the same - same RB circuits and same thermal error.\n",
+    "\n",
+    "this way we can see the that the changes in the epsilon map correspond to the changes in the noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noise_model2 = NoiseModel()\n",
+    "\n",
+    "noise_model2.add_nonlocal_quantum_error(error, basis_gates, [0], [0, 3])\n",
+    "noise_model2.add_nonlocal_quantum_error(error, basis_gates, [3], [3, 0])\n",
+    "\n",
+    "\n",
+    "# Add T1/T2 noise to the simulation\n",
+    "t_1 = 10\n",
+    "t_2 = 20\n",
+    "gate1q = 0.1\n",
+    "gate2q = 0.5\n",
+    "noise_model2.add_all_qubit_quantum_error(thermal_relaxation_error(t_1, t_2, gate1q), 'u2')\n",
+    "noise_model2.add_all_qubit_quantum_error(thermal_relaxation_error(t_1, t_2, 2 * gate1q), 'u3')\n",
+    "noise_model2.add_all_qubit_quantum_error(\n",
+    "    thermal_relaxation_error(t_1, t_2, gate2q).tensor(thermal_relaxation_error(t_1, t_2, gate2q)), 'cx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finished seed 0\n",
+      "finished seed 1\n",
+      "finished seed 2\n",
+      "finished seed 3\n",
+      "finished seed 4\n",
+      "finished seed 5\n",
+      "finished seed 6\n",
+      "finished seed 7\n",
+      "finished seed 8\n",
+      "finished seed 9\n",
+      "finished seed 10\n",
+      "finished seed 11\n",
+      "finished seed 12\n",
+      "finished seed 13\n",
+      "finished seed 14\n",
+      "finished seed 15\n",
+      "finished seed 16\n",
+      "finished seed 17\n",
+      "finished seed 18\n",
+      "finished seed 19\n",
+      "Finished Running\n"
+     ]
+    }
+   ],
+   "source": [
+    "results2 = []\n",
+    "for circ_ind, circuit in enumerate(rb_circs):\n",
+    "    results2.append(qiskit.execute(circuit, backend=backend,\n",
+    "                                      basis_gates=basis_gates,\n",
+    "                                      shots=shots,\n",
+    "                                      noise_model=noise_model2,\n",
+    "                                      seed_simulator=42).result())   \n",
+    "    print(\"finished seed %s\" % str(circ_ind))\n",
+    "print(\"Finished Running\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#correct results\n",
+    "result_corr2 = []\n",
+    "for result in results2:\n",
+    "    result_corr2.append(meas_filter.apply(result))\n",
+    "\n",
+    "rbfit2 = CorrelatedRBFitter(result_corr2, xdata, rb_opts['rb_pattern'])\n",
+    "\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "axis = [plt.subplot(1, 2, 1), plt.subplot(1, 2, 2)]\n",
+    "    \n",
+    "for i,j in enumerate(np.arange(5,7)):\n",
+    "    pattern_ind = j\n",
+    "\n",
+    "    # Plot the essence by calling plot_rb_data\n",
+    "    rbfit2.plot_rb_data(pattern_ind, ax=axis[i], add_label=True, show_plt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Qubits [0, 1, 2, 3] on Backend qasm_simulator')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit2.fit_alphas\n",
+    "rbfit2.fit_alphas_to_epsilon()\n",
+    "#Plot the depolarizing probabilities\n",
+    "fig = plt.figure(figsize=(14, 6))\n",
+    "rbfit2.plot_epsilon(ax=fig.gca())\n",
+    "fig.gca().set_title(\"Qubits %s on Backend %s\"%(str(qubit_list),backend.name()), fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check different pattern\n",
+    "with patterns with different subsystem sizes there seems to have a lot of statistical correlations. the fitter finds high \"crosstalk\" errors even when no crosstalk noise injected at all. still checking if it is bug, or there is really a high correlations within multi-qubit patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Number of seeds (random sequences)\n",
+    "nseeds = 20 #set to 20 for testing\n",
+    "#Number of Cliffords in the sequence (start, stop, steps)\n",
+    "nCliffs2 = list(np.arange(1,600,50))\n",
+    "\n",
+    "#1Q RB on Q0-3 simultaneously\n",
+    "rb_pattern2 = [[0,1],[2],[3]]\n",
+    "\n",
+    "rb_opts2 = {}\n",
+    "rb_opts2['length_vector'] = nCliffs2\n",
+    "rb_opts2['nseeds'] = nseeds\n",
+    "rb_opts2['rb_pattern'] = rb_pattern2\n",
+    "rb_opts['rand_seed'] = 0\n",
+    "rb_circs2, xdata2 = rb.randomized_benchmarking_seq(**rb_opts2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error\n",
+    "\n",
+    "# create noise model\n",
+    "noise_model3 = NoiseModel()\n",
+    "\n",
+    "p1Q = 0.001\n",
+    "p2Q = 0.01\n",
+    "\n",
+    "noise_model3.add_all_qubit_quantum_error(depolarizing_error(p1Q, 1), 'u2')\n",
+    "noise_model3.add_all_qubit_quantum_error(depolarizing_error(2 * p1Q, 1), 'u3')\n",
+    "noise_model3.add_all_qubit_quantum_error(depolarizing_error(p2Q, 2), 'cx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finished seed 0\n",
+      "finished seed 1\n",
+      "finished seed 2\n",
+      "finished seed 3\n",
+      "finished seed 4\n",
+      "finished seed 5\n",
+      "finished seed 6\n",
+      "finished seed 7\n",
+      "finished seed 8\n",
+      "finished seed 9\n",
+      "finished seed 10\n",
+      "finished seed 11\n",
+      "finished seed 12\n",
+      "finished seed 13\n",
+      "finished seed 14\n",
+      "finished seed 15\n",
+      "finished seed 16\n",
+      "finished seed 17\n",
+      "finished seed 18\n",
+      "finished seed 19\n",
+      "Finished Running\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run the noisy simulation (with fixed seed)\n",
+    "# use the same noise as the first run\n",
+    "results3 = []\n",
+    "for circ_ind, circuit in enumerate(rb_circs2):\n",
+    "    results3.append(qiskit.execute(circuit, backend=backend,\n",
+    "                                      basis_gates=basis_gates,\n",
+    "                                      shots=shots,\n",
+    "                                      noise_model=noise_model3,\n",
+    "                                      seed_simulator=42).result())   \n",
+    "    print(\"finished seed %s\" % str(circ_ind))\n",
+    "print(\"Finished Running\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit3 = CorrelatedRBFitter(results3, xdata2, rb_opts2['rb_pattern'])\n",
+    "\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "axis = [plt.subplot(1, 2, 1), plt.subplot(1, 2, 2)]\n",
+    "    \n",
+    "for i,j in enumerate(np.arange(1,3)):\n",
+    "    pattern_ind = j\n",
+    "\n",
+    "    # Plot the essence by calling plot_rb_data\n",
+    "    rbfit3.plot_rb_data(pattern_ind, ax=axis[i], add_label=True, show_plt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Qubits [[0, 1], [2], [3]] on Backend qasm_simulator')"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit3.fit_alphas\n",
+    "rbfit3.fit_alphas_to_epsilon()\n",
+    "#Plot the depolarizing probabilities\n",
+    "fig = plt.figure(figsize=(14, 6))\n",
+    "rbfit3.plot_epsilon(ax=fig.gca())\n",
+    "fig.gca().set_title(\"Qubits %s on Backend %s\"%(str(rb_opts2['rb_pattern']),backend.name()), fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "run again, but this time with crosstalk between qubits 1 and 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create noise model\n",
+    "noise_model4 = NoiseModel()\n",
+    "\n",
+    "zz_unitary = np.eye(4,dtype=complex)\n",
+    "zz_unitary[3,3] = np.exp(0.5j * (np.pi / 5))\n",
+    "error = coherent_unitary_error(zz_unitary)\n",
+    "\n",
+    "noise_model4.add_nonlocal_quantum_error(error, basis_gates, [1], [1, 2])\n",
+    "noise_model4.add_nonlocal_quantum_error(error, basis_gates, [2], [2, 1])\n",
+    "\n",
+    "p1Q = 0.0005\n",
+    "p2Q = 0.005\n",
+    "\n",
+    "noise_model4.add_all_qubit_quantum_error(depolarizing_error(p1Q, 1), 'u2')\n",
+    "noise_model4.add_all_qubit_quantum_error(depolarizing_error(2 * p1Q, 1), 'u3')\n",
+    "noise_model4.add_all_qubit_quantum_error(depolarizing_error(p2Q, 2), 'cx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finished seed 0\n",
+      "finished seed 1\n",
+      "finished seed 2\n",
+      "finished seed 3\n",
+      "finished seed 4\n",
+      "finished seed 5\n",
+      "finished seed 6\n",
+      "finished seed 7\n",
+      "finished seed 8\n",
+      "finished seed 9\n",
+      "finished seed 10\n",
+      "finished seed 11\n",
+      "finished seed 12\n",
+      "finished seed 13\n",
+      "finished seed 14\n",
+      "finished seed 15\n",
+      "finished seed 16\n",
+      "finished seed 17\n",
+      "finished seed 18\n",
+      "finished seed 19\n",
+      "Finished Running\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run the noisy simulation (with fixed seed)\n",
+    "# use the same noise as the first run\n",
+    "results4 = []\n",
+    "for circ_ind, circuit in enumerate(rb_circs2):\n",
+    "    results4.append(qiskit.execute(circuit, backend=backend,\n",
+    "                                      basis_gates=basis_gates,\n",
+    "                                      shots=shots,\n",
+    "                                      noise_model=noise_model4,\n",
+    "                                      seed_simulator=42).result())   \n",
+    "    print(\"finished seed %s\" % str(circ_ind))\n",
+    "print(\"Finished Running\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit4 = CorrelatedRBFitter(results4, xdata2, rb_opts2['rb_pattern'])\n",
+    "\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "axis = [plt.subplot(1, 2, 1), plt.subplot(1, 2, 2)]\n",
+    "    \n",
+    "for i,j in enumerate(np.arange(1,3)):\n",
+    "    pattern_ind = j\n",
+    "\n",
+    "    # Plot the essence by calling plot_rb_data\n",
+    "    rbfit4.plot_rb_data(pattern_ind, ax=axis[i], add_label=True, show_plt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Qubits [[0, 1], [2], [3]] on Backend qasm_simulator')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit4.fit_alphas\n",
+    "rbfit4.fit_alphas_to_epsilon()\n",
+    "#Plot the depolarizing probabilities\n",
+    "fig = plt.figure(figsize=(14, 6))\n",
+    "rbfit4.plot_epsilon(ax=fig.gca())\n",
+    "fig.gca().set_title(\"Qubits %s on Backend %s\"%(str(rb_opts2['rb_pattern']),backend.name()), fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "when all the subsystems are the same size, these correlations are smaller (but still not negligible)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rb_pattern3 = [[0,1],[2, 3]]\n",
+    "rb_opts3 = {}\n",
+    "rb_opts3['length_vector'] = nCliffs2\n",
+    "rb_opts3['nseeds'] = nseeds\n",
+    "rb_opts3['rb_pattern'] = rb_pattern3\n",
+    "rb_opts['rand_seed'] = 0\n",
+    "rb_circs3, xdata3 = rb.randomized_benchmarking_seq(**rb_opts3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finished seed 0\n",
+      "finished seed 1\n",
+      "finished seed 2\n",
+      "finished seed 3\n",
+      "finished seed 4\n",
+      "finished seed 5\n",
+      "finished seed 6\n",
+      "finished seed 7\n",
+      "finished seed 8\n",
+      "finished seed 9\n",
+      "finished seed 10\n",
+      "finished seed 11\n",
+      "finished seed 12\n",
+      "finished seed 13\n",
+      "finished seed 14\n",
+      "finished seed 15\n",
+      "finished seed 16\n",
+      "finished seed 17\n",
+      "finished seed 18\n",
+      "finished seed 19\n",
+      "Finished Running\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run the noisy simulation (with fixed seed)\n",
+    "# use the same noise as the first run\n",
+    "results5 = []\n",
+    "for circ_ind, circuit in enumerate(rb_circs3):\n",
+    "    results5.append(qiskit.execute(circuit, backend=backend,\n",
+    "                                      basis_gates=basis_gates,\n",
+    "                                      shots=shots,\n",
+    "                                      noise_model=noise_model3,\n",
+    "                                      seed_simulator=42).result())   \n",
+    "    print(\"finished seed %s\" % str(circ_ind))\n",
+    "print(\"Finished Running\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit5 = CorrelatedRBFitter(results5, xdata3, rb_opts3['rb_pattern'])\n",
+    "\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "axis = [plt.subplot(1, 2, 1), plt.subplot(1, 2, 2)]\n",
+    "    \n",
+    "for i,j in enumerate(np.arange(1,3)):\n",
+    "    pattern_ind = j\n",
+    "\n",
+    "    # Plot the essence by calling plot_rb_data\n",
+    "    rbfit5.plot_rb_data(pattern_ind, ax=axis[i], add_label=True, show_plt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Qubits [[0, 1], [2, 3]] on Backend qasm_simulator')"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit5.fit_alphas\n",
+    "rbfit5.fit_alphas_to_epsilon()\n",
+    "#Plot the depolarizing probabilities\n",
+    "fig = plt.figure(figsize=(14, 6))\n",
+    "rbfit5.plot_epsilon(ax=fig.gca())\n",
+    "fig.gca().set_title(\"Qubits %s on Backend %s\"%(str(rb_opts3['rb_pattern']),backend.name()), fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "run again, but this time with crosstalk between qubits 1 and 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finished seed 0\n",
+      "finished seed 1\n",
+      "finished seed 2\n",
+      "finished seed 3\n",
+      "finished seed 4\n",
+      "finished seed 5\n",
+      "finished seed 6\n",
+      "finished seed 7\n",
+      "finished seed 8\n",
+      "finished seed 9\n",
+      "finished seed 10\n",
+      "finished seed 11\n",
+      "finished seed 12\n",
+      "finished seed 13\n",
+      "finished seed 14\n",
+      "finished seed 15\n",
+      "finished seed 16\n",
+      "finished seed 17\n",
+      "finished seed 18\n",
+      "finished seed 19\n",
+      "Finished Running\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run the noisy simulation (with fixed seed)\n",
+    "# use the same noise as the first run\n",
+    "results6 = []\n",
+    "for circ_ind, circuit in enumerate(rb_circs3):\n",
+    "    results6.append(qiskit.execute(circuit, backend=backend,\n",
+    "                                      basis_gates=basis_gates,\n",
+    "                                      shots=shots,\n",
+    "                                      noise_model=noise_model4,\n",
+    "                                      seed_simulator=42).result())   \n",
+    "    print(\"finished seed %s\" % str(circ_ind))\n",
+    "print(\"Finished Running\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit6 = CorrelatedRBFitter(results6, xdata3, rb_opts3['rb_pattern'])\n",
+    "\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "axis = [plt.subplot(1, 2, 1), plt.subplot(1, 2, 2)]\n",
+    "    \n",
+    "for i,j in enumerate(np.arange(1,3)):\n",
+    "    pattern_ind = j\n",
+    "\n",
+    "    # Plot the essence by calling plot_rb_data\n",
+    "    rbfit6.plot_rb_data(pattern_ind, ax=axis[i], add_label=True, show_plt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Qubits [[0, 1], [2, 3]] on Backend qasm_simulator')"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rbfit6.fit_alphas\n",
+    "rbfit6.fit_alphas_to_epsilon()\n",
+    "#Plot the depolarizing probabilities\n",
+    "fig = plt.figure(figsize=(14, 6))\n",
+    "rbfit6.plot_epsilon(ax=fig.gca())\n",
+    "fig.gca().set_title(\"Qubits %s on Backend %s\"%(str(rb_opts3['rb_pattern']),backend.name()), fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/qiskit/ignis/verification/randomized_benchmarking/fitters.py b/qiskit/ignis/verification/randomized_benchmarking/fitters.py
index 587699126..34a5714ae 100644
--- a/qiskit/ignis/verification/randomized_benchmarking/fitters.py
+++ b/qiskit/ignis/verification/randomized_benchmarking/fitters.py
@@ -24,7 +24,7 @@
 """
 
 from abc import ABC, abstractmethod
-from scipy.optimize import curve_fit
+from scipy.optimize import curve_fit, least_squares
 import numpy as np
 from qiskit import QiskitError
 from qiskit.quantum_info.analysis.average import average_data
@@ -1426,3 +1426,544 @@ def plot_rb_data(self, pattern_index=0, ax=None,
 
         if show_plt:
             plt.show()
+
+
+class CorrelatedRBFitter(RBFitterBase):
+    """
+        Class for fitters for correlated randomized benchmarking
+    """
+
+    def __init__(self, backend_result, cliff_lengths, rb_pattern):
+        """
+        Args:
+            backend_result (list): list of results (qiskit.Result).
+            cliff_lengths (list): the Clifford lengths, 2D list i x j where i is the
+                number of patterns, j is the number of cliffords lengths
+            rb_pattern (list): the pattern for the rb sequences.
+            will be added - max_weight (int): maximum weight to fit (-1 fit all)
+        """
+
+        # WARNING: Haven't implemented max_weight, max_distance yet!!
+
+        # calculate all possible subsystem strings
+        self._n_partitions = len(rb_pattern)
+        self._n_subsystems = 2**self._n_partitions-1
+        self._subsystems = []
+        self._subsystems2 = []  # int representation
+        for i in range(self._n_subsystems):
+            # this gives the correlator in the subsystem representation
+            self._subsystems.append(("{0:0%db}" % self._n_partitions).format(i+1))
+
+            # expand into the full qubit representation and save as an integer
+            tmplist = []
+            for ii in range(self._n_partitions):
+                for _ in range(len(rb_pattern[ii])):
+                    # the qubits order is from right to left, while the subsystem's list
+                    # positioning of a number is from left to right
+                    tmplist.insert(0, self._subsystems[-1][-(ii + 1)])
+            self._subsystems2.append(int(''.join(tmplist), base=2))
+
+        self._r_coeff = None
+
+        # clifford_lengths need to be the same in all patterns
+        self._cliff_lengths = cliff_lengths
+
+        self._rb_pattern = rb_pattern
+        self._nq = sum([len(i) for i in rb_pattern])
+
+        statedict = {("{0:0%db}" % self._nq).format(i): 1 for i in range(2**self._nq)}
+
+        self._zdict_ops = []
+        for subsys in self._subsystems2:
+            self._zdict_ops.append(statedict.copy())
+            for i in range(2**self._nq):
+                # if the operator (expressed as an integer) and the state
+                # overlap is even then the z operator is '1' otherwise it's -1
+                if bin(i & subsys).count('1') % 2 != 0:
+                    self._zdict_ops[-1][("{0:0%db}" % self._nq).format(i)] = -1
+
+        self._raw_data = []
+        self._ydata = []
+        self._fit = [{} for e in self._subsystems]
+        self._epsilons = []
+        self._nseeds = []
+        self._circ_name_type = ''
+
+        self._result_list = []
+        self.add_data(backend_result)
+
+    @property
+    def raw_data(self):
+        """Return raw data."""
+        return self._raw_data
+
+    @property
+    def cliff_lengths(self):
+        """Return clifford lengths."""
+        return self.cliff_lengths
+
+    @property
+    def ydata(self):
+        """Return ydata (means and std devs)."""
+        return self._ydata
+
+    @property
+    def fit(self):
+        """Return fit, list."""
+        return self._fit
+
+    @property
+    def fit_alphas(self):
+        """Return alphas, dict (subsystems in int representation as keys and alphas as values)."""
+        alphadict = {}
+        for i, j in enumerate(self._subsystems):
+            alphadict[j] = self.fit[i]['params'][1]
+        return alphadict
+
+    @property
+    def fit_epsilon(self):
+        """Return epsilons, dict (subsystems in int representation as keys list of
+        [epsilon, estimation_error] as values)."""
+        return self._epsilons
+
+    @property
+    def rb_fit_fun(self):
+        """Return the function rb_fit_fun."""
+        return self._rb_fit_fun
+
+    @property
+    def seeds(self):
+        """Return the number of loaded seeds."""
+        return self._nseeds
+
+    @property
+    def results(self):
+        """Return all the results."""
+        return self._result_list
+
+    def add_data(self, new_backend_result, rerun_fit=True):
+        """
+        Add a new result. Re calculate the raw data, means and
+        fit.
+        Args:
+            new_backend_result (list): list of rb results
+            rerun_fit (bool): whether to re-caculate the means and fit the result
+        Additional information:
+            Assumes that 'new_backend_result' is the output of execution of circuits
+            generated by randomized_becnhmarking_seq
+        """
+        if new_backend_result is None:
+            return
+
+        if not isinstance(new_backend_result, list):
+            new_backend_result = [new_backend_result]
+
+        for result in new_backend_result:
+            self._result_list.append(result)
+
+            # update the number of seeds *if* new ones added.
+            # Note, no checking if we've done all the cliffords
+            for rbcirc in result.results:
+                nseeds_circ = int(rbcirc.header.name.split('_')[-1])
+                if nseeds_circ not in self._nseeds:
+                    self._nseeds.append(nseeds_circ)
+
+#        for result in self._result_list:
+#            if not len(result.results) == len(self._cliff_lengths[0]):
+#                raise ValueError(
+#                    "The number of clifford lengths must match the number of "
+#                    "results")
+
+        if rerun_fit:
+            self.calc_data()
+            self.calc_statistics()
+            self.fit_data()
+
+    @staticmethod
+    def _rb_fit_fun(x, a, alpha, b):
+        """Function used to fit rb."""
+        # pylint: disable=invalid-name
+        return a * alpha ** x + b
+
+    def calc_data(self):
+        """
+        Measure the correlators into an internal variable _raw_data, which is a 3-dimensional
+        list, where item (i,j,k) is the probability to measure the "i" correlator for
+        seed no. j and vector length self._cliff_lengths[i][k].
+        Additional information:
+            Assumes that 'self._result_list' is the output of execution of circuits
+            generated by randomized_becnhmarking_seq
+        """
+
+        # The type of the circuit name, e.g. rb or rb_interleaved
+        # as it appears in the result (before _length_%d_seed_%d)
+        self._circ_name_type = self._result_list[0].results[0]. \
+            header.name.split("_length")[0]
+
+        circ_counts = {}
+        circ_shots = {}
+        for seed in self._nseeds:
+            for circ, _ in enumerate(self._cliff_lengths[0]):
+                circ_name = self._circ_name_type + '_length_%d_seed_%d' \
+                            % (circ, seed)
+                count_list = []
+                for result in self._result_list:
+                    try:
+                        count_list.append(result.get_counts(circ_name))
+                    except (QiskitError, KeyError):
+                        pass
+
+                circ_counts[circ_name] = \
+                    build_counts_dict_from_list(count_list)
+
+                circ_shots[circ_name] = sum(circ_counts[circ_name].values())
+
+        self._raw_data = []
+
+        subsystems_n = len(self._subsystems)
+        for sub_ind in range(subsystems_n):
+
+            self._raw_data.append([])
+
+            for seedidx, seed in enumerate(self._nseeds):
+
+                self._raw_data[-1].append([])
+                for k, _ in enumerate(self._cliff_lengths[0]):
+                    circ_name = self._circ_name_type + '_length_%d_seed_%d' \
+                                % (k, seed)
+
+                    zcorr = average_data(circ_counts[circ_name],
+                                         self._zdict_ops[sub_ind])
+
+                    self._raw_data[-1][seedidx].append(zcorr)
+
+    def calc_statistics(self):
+        """
+        Extract averages and std dev from the raw data (self._raw_data).
+        Assumes that self._calc_data has been run. Output into internal _ydata variable:
+            ydata is a list of dictionaries (length number of patterns).
+            Dictionary ydata[i]:
+            ydata[i]['mean'] is a numpy_array of length n;
+                        entry j of this array contains the mean probability of
+                        success over seeds, for vector length self._cliff_lengths[i][j].
+            And ydata[i]['std'] is a numpy_array of length n;
+                        entry j of this array contains the std of the probability of
+                        success over seeds, for vector length self._cliff_lengths[i][j].
+        """
+
+        self._ydata = []
+        for sub_ind in range(len(self._subsystems)):
+            self._ydata.append({})
+            self._ydata[-1]['mean'] = np.mean(self._raw_data[sub_ind], 0)
+
+            if len(self._raw_data[sub_ind]) == 1:  # 1 seed
+                self._ydata[-1]['std'] = None
+            else:
+                self._ydata[-1]['std'] = np.std(self._raw_data[sub_ind], 0)
+
+    def fit_data_pattern(self, patt_ind, fit_guess):
+        """
+        Fit the RB results of a particular pattern to an exponential curve.
+        For correlated RB this means fit for a particular subsystem
+        Args:
+            patt_ind (int): index of the subsystem to fit
+            fit_guess (array_like): guess values for the fit (length 3)
+        Puts the results into a list of fit dictionaries:
+            where each dictionary corresponds to a pattern and has fields:
+            'params' - three parameters of rb_fit_fun. The middle one is the exponent.
+            'params_err' - the error limits of the parameters.
+        """
+
+        lens = self._cliff_lengths[0]
+
+        # if at least one of the std values is zero, then sigma is replaced by None
+        if not self._ydata[patt_ind]['std'] is None:
+            sigma = self._ydata[patt_ind]['std'].copy()
+            if len(sigma) - np.count_nonzero(sigma) > 0:
+                sigma = None
+        else:
+            sigma = None
+        params, pcov = curve_fit(self._rb_fit_fun, lens,
+                                 self._ydata[patt_ind]['mean'],
+                                 sigma=sigma,
+                                 p0=fit_guess,
+                                 bounds=([-2, 0, -0.5], [2, 1, 0.5]))
+
+        params_err = np.sqrt(np.diag(pcov))
+
+        self._fit[patt_ind] = {'params': params, 'params_err': params_err}
+
+    def fit_data(self):
+        """
+        Fit the RB results to an exponential curve, by fitting each of the
+        subsystems (z correlators).
+        Use the data to construct guess values for the fits.
+        Puts the results into a list of fit dictionaries:
+            where each dictionary corresponds to a pattern and has fields:
+            'params' - three parameters of rb_fit_fun. The middle one is the exponent.
+            'params_err' - the error limits of the parameters.
+        """
+
+        for sub_ind, _ in enumerate(self._subsystems):
+            fit_guess = [0.95, 0.99, 0.0]
+            fit_guess[0] = self._ydata[sub_ind]['mean'][0]
+            # Should decay to 1/2^n
+            fit_guess[2] = 0
+
+            # Use the first two points to guess the decay param
+            y0 = self._ydata[sub_ind]['mean'][0]
+            y1 = self._ydata[sub_ind]['mean'][1]
+            dcliff = (self._cliff_lengths[0][1] -
+                      self._cliff_lengths[0][0])
+            dy = ((y1 - fit_guess[2]) /
+                  (y0 - fit_guess[2]))
+            alpha_guess = dy**(1/dcliff)
+            if alpha_guess < 1.0:
+                fit_guess[1] = alpha_guess
+
+            fit_guess[0] = ((y0 - fit_guess[2]) /
+                            fit_guess[1]**self._cliff_lengths[0][0])
+            self.fit_data_pattern(sub_ind, tuple(fit_guess))
+
+    def plot_rb_data(self, sub_index=0, ax=None,
+                     add_label=True, show_plt=True):
+        """
+        Plot randomized benchmarking data of a single subsystem.
+        Args:
+            sub_index (int): which RB subsystem to plot
+            ax (Axes or None): plot axis (if passed in).
+            add_label (bool): Add an EPC label
+            show_plt (bool): display the plot.
+        Raises:
+            ImportError: If matplotlib is not installed.
+        """
+
+        fit_function = self._rb_fit_fun
+
+        if not HAS_MATPLOTLIB:
+            raise ImportError('The function plot_rb_data needs matplotlib. '
+                              'Run "pip install matplotlib" before.')
+
+        if ax is None:
+            plt.figure()
+            ax = plt.gca()
+
+        xdata = self._cliff_lengths[0]
+
+        # Plot the result for each sequence
+        for one_seed_data in self._raw_data[sub_index]:
+            ax.plot(xdata, one_seed_data, color='gray', linestyle='none',
+                    marker='x')
+
+        # Plot the mean with error bars
+        ax.errorbar(xdata, self._ydata[sub_index]['mean'],
+                    yerr=self._ydata[sub_index]['std'],
+                    color='r', linestyle='--', linewidth=3)
+
+        # Plot the fit
+        ax.plot(xdata,
+                fit_function(xdata, *self._fit[sub_index]['params']),
+                color='blue', linestyle='-', linewidth=2)
+        ax.tick_params(labelsize=14)
+
+        ax.set_xlabel('Clifford Length', fontsize=16)
+        ax.set_ylabel('Ground State Population', fontsize=16)
+        ax.set_title('Plotting %s Correlator' % self._subsystems[sub_index])
+        ax.grid(True)
+
+        if add_label:
+            bbox_props = dict(boxstyle="round,pad=0.3",
+                              fc="white", ec="black", lw=2)
+
+            ax.text(0.6, 0.9,
+                    "alpha: %.3f(%.1e)" %
+                    (self._fit[sub_index]['params'][1],
+                     self._fit[sub_index]['params_err'][1]),
+                    ha="center", va="center", size=14,
+                    bbox=bbox_props, transform=ax.transAxes)
+
+        if show_plt:
+            plt.show()
+
+    def _precalc_r_coeff(self):
+        """
+        Precalculate the matrices that relate alpha to epsilon
+        alpha_k  = Prod_j (1 + R_kj * eps_j)
+        """
+
+        # precompute the matrix of coefficients for calculating the rauli's
+        # from this matrix alpha_k = prod[1-rauli_matrix_coeff*e] where e is the error vector
+        self._r_coeff = np.zeros([len(self._subsystems), len(self._subsystems)], dtype=float)
+        for (ii, subspace1) in enumerate(self._subsystems):
+            for (kk, subspace2) in enumerate(self._subsystems):
+
+                depol1 = self._depol_Rauli([subspace2], subspace1, 1.)[0]
+                depol2 = self._depol_Rauli([subspace2], subspace1, 0.)[0]
+
+                self._r_coeff[ii, kk] = depol1-depol2
+
+    def _depol_Rauli(self, pauli_elem, depol_subsys, p):
+        """
+        Calculate the rauli of the fixed weight depolarizing map
+        e.g. rho' = (1-e)*rho + e*II/d (II in the depol_subspace subspace only!)
+        Args:
+            pauli_elem (list): List of the pauli elements of the Rauli.
+            This is a string of 1s and 0s which map to Z and I
+            e.g. '101' -> Pauli ZIZ
+            depol_subsys (list): The subsystem that is depolarizing (in the same
+            format as pauli_elem)
+            p (int): Depolarizing probability ([0,1])
+        Returns:
+            list: list of the rauli elements for pauli_elem
+        Additional Info:
+            The rauli's for a depolarizing map can only be 1 or 1-p
+        """
+
+        rauli = np.zeros(len(pauli_elem), dtype=float)
+        for (state_index, state_paulis) in enumerate(pauli_elem):
+            all_id = 1
+            for j, state_pauli in enumerate(state_paulis):
+                if depol_subsys[j] == '1' and state_pauli == '1':
+                    all_id = 0
+            rauli[state_index] = (1.0-p) + p*all_id
+
+        return rauli
+
+    def _calc_alphas(self, eps_list):
+        """
+        Calculate the alpha from the fixed weight depolarizing errors
+        The coefficient matrix has been precalculated
+        Args:
+            eps_list (list): list of epsilons from the error model (ordered
+            using the self._subsystems list)
+        Returns:
+            list: List of alphas given eps_list
+        """
+
+        eps_len = len(eps_list)
+        alpha_calc = np.ones(eps_len, dtype=float)
+
+        for ii in range(eps_len):
+            for jj in range(eps_len):
+                alpha_calc[ii] *= (1.0+self._r_coeff[jj, ii]*eps_list[jj])
+        return alpha_calc
+
+    def _alpha_diff_func(self, eps_list):
+        """
+        Calculate the difference between the experimental alphas and
+        the alphas calculated from the values in eps_dict
+        Args:
+            eps_list (list): list of epsilons from the error model (ordered
+            using the self._subsystems list)
+        Returns:
+            list: List of the differences between the calculated and measured alphas
+        """
+
+        alpha_calc_list = self._calc_alphas(eps_list)
+        diff_list = [self.fit_alphas[self._subsystems[i]]-alpha_calc_list[i] for i
+                     in range(len(self._subsystems))]
+
+        return diff_list
+
+    def fit_alphas_to_epsilon(self, init_guess=None):
+        """
+        Fit the set of decay coefficients to the fixed weight depolarizing
+        map to get the set of epsilon
+        Args:
+            init_guess (array_like): starting point for the fit for each epsilon.
+            If none will use 0.01 for each epsilon.
+        Returns:
+            int: cost value of the optimization
+        """
+
+        if self._r_coeff is None:
+            # precalculate
+            self._precalc_r_coeff()
+
+        if init_guess is None:
+            init_guess = 0.01*np.ones(len(self.fit_alphas.keys()), dtype=float)
+
+        optm_results = least_squares(self._alpha_diff_func,
+                                     init_guess, bounds=[0, 1.0])
+
+        est_err = np.sqrt(optm_results.cost *
+                          np.diag(np.linalg.inv(np.dot(
+                              np.transpose(optm_results.jac),
+                              optm_results.jac)))/len(init_guess))
+
+        self._epsilons = {}
+        for i, j in enumerate(self._subsystems):
+            self._epsilons[j] = [optm_results.x[i], est_err[i]]
+
+        return optm_results.cost
+
+    def plot_epsilon(self, ax=None):
+        """
+        Plot the set of epsilons (need to run fit_epsilon first)
+        """
+
+        if not HAS_MATPLOTLIB:
+            raise ImportError('The function plot_rb_data needs matplotlib. '
+                              'Run "pip install matplotlib" before.')
+
+        if ax is None:
+            plt.figure()
+            ax = plt.gca()
+
+        # this is the data as a dictionary
+        eps_data = self.fit_epsilon
+
+        corr_list = list(eps_data.keys())
+        eps_list = []
+        eps_err_list = []
+        for corr in corr_list:
+            eps_list.append(eps_data[corr][0])
+            eps_err_list.append(eps_data[corr][1])
+
+        # sort based on weight
+        corr_weight = [i.count('1') for i in corr_list]
+        sort_args = np.argsort(corr_weight)
+
+        corr_list = np.array(corr_list)[sort_args]
+        eps_list = np.array(eps_list)[sort_args]
+        eps_err_list = np.array(eps_err_list)[sort_args]
+        nq = len(corr_list[0])
+
+        if nq < 6:
+            # for small amount of qubits - use the same colors each time
+            color_list = ['black', 'red', 'blue', 'green', 'yellow']
+        else:
+            # for larger amount of epsilon - sample colors from color map
+            # the size of the color map is the number of qubits
+            color_list = plt.cm.get_cmap("Set1", nq)
+        prev_weight = 0
+        for ii, jj in enumerate(corr_list):
+            point_weight = jj.count('1')
+            if prev_weight != point_weight:
+                legend_label = 'Weight %d' % point_weight
+                prev_weight = point_weight
+            else:
+                legend_label = '_nolegend_'
+
+            if nq < 6:
+                ax.errorbar(ii + 1, eps_list[ii], yerr=eps_err_list[ii],
+                            label=legend_label, linestyle='none',
+                            marker='.', markersize=20,
+                            color=color_list[point_weight - 1])
+            else:
+                ax.errorbar(ii + 1, eps_list[ii], yerr=eps_err_list[ii],
+                            label=legend_label, linestyle='none',
+                            marker='.', markersize=20,
+                            color=color_list(point_weight - 1))
+
+        ax.legend(fontsize=16)
+        ax.set_xlim([0, len(corr_weight) + 1])
+        ax.set_ylim([0 - np.max(eps_err_list), np.max(eps_list) + np.max(eps_err_list)])
+        ax.grid()
+        # add x-tick labels
+        ax.set_xticks([y + 1 for y in range(len(corr_weight))])
+        ax.set_xticklabels(corr_list)
+        ax.get_figure().autofmt_xdate()
+        ax.set_ylabel('Probability of Error', fontsize=16)
+        ax.set_xlabel('Subsystem Correlator', fontsize=16)
+        ax.tick_params(labelsize=14)
diff --git a/test/rb/generate_data.py b/test/rb/generate_data.py
index 5512327c2..7fc5ef56f 100644
--- a/test/rb/generate_data.py
+++ b/test/rb/generate_data.py
@@ -23,7 +23,7 @@
 import qiskit
 import qiskit.ignis.verification.randomized_benchmarking as rb
 from qiskit.ignis.verification.randomized_benchmarking import \
-    RBFitter, InterleavedRBFitter, PurityRBFitter, CNOTDihedralRBFitter
+    RBFitter, InterleavedRBFitter, PurityRBFitter, CNOTDihedralRBFitter, CorrelatedRBFitter
 from qiskit.providers.aer.noise import NoiseModel
 from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error,\
     thermal_relaxation_error, coherent_unitary_error
@@ -255,6 +255,102 @@ def rb_purity_circuit_execution(rb_opts: dict, shots: int):
     return purity_results, xdata, npurity, coherent_results
 
 
+def rb_correlated_circuit_exec1(rb_opts: dict, shots: int):
+    """
+    Create correlated rb circuits with coherent weight 2 error and
+     thermal relaxation error and simulate them
+
+    Args:
+        rb_opts: the options for the rb circuits
+        shots: number of shots for each circuit simulation
+
+    Returns:
+        list: list of Results of the circuits simulations
+        list: the xdata of the rb circuit
+
+    """
+    # Load simulator
+    backend = qiskit.Aer.get_backend('qasm_simulator')
+    basis_gates = ['u1', 'u2', 'u3', 'cx']
+
+    rb_circs, xdata = rb.randomized_benchmarking_seq(**rb_opts)
+
+    # creating the noise
+    noise_model = NoiseModel()
+    zz_unitary = np.eye(4, dtype=complex)
+    zz_unitary[3, 3] = np.exp(0.5j * (np.pi / 5))
+    error = coherent_unitary_error(zz_unitary)
+    noise_model.add_nonlocal_quantum_error(error, basis_gates, [1], [1, 2])
+    noise_model.add_nonlocal_quantum_error(error, basis_gates, [2], [2, 1])
+
+    # Add T1/T2 noise to the simulation
+    t_1 = 10
+    t_2 = 20
+    gate1q = 0.1
+    gate2q = 0.5
+    noise_model.add_all_qubit_quantum_error(thermal_relaxation_error(t_1, t_2, gate1q), 'u2')
+    noise_model.add_all_qubit_quantum_error(thermal_relaxation_error(t_1, t_2, 2 * gate1q), 'u3')
+    noise_model.add_all_qubit_quantum_error(thermal_relaxation_error(t_1, t_2, gate2q).tensor(
+        thermal_relaxation_error(t_1, t_2, gate2q)), 'cx')
+
+    results = []
+    for circuit in rb_circs:
+        results.append(qiskit.execute(circuit, backend=backend,
+                                      basis_gates=basis_gates,
+                                      shots=shots,
+                                      noise_model=noise_model,
+                                      seed_simulator=SEED).result())
+
+    return results, xdata
+
+
+def rb_correlated_circuit_exec2(rb_opts: dict, shots: int):
+    """
+    Create correlated rb circuits with coherent weight 2 error and
+     thermal relaxation error and simulate them
+
+    Args:
+        rb_opts: the options for the rb circuits
+        shots: number of shots for each circuit simulation
+
+    Returns:
+        list: list of Results of the circuits simulations
+        list: the xdata of the rb circuit
+
+    """
+    # Load simulator
+    backend = qiskit.Aer.get_backend('qasm_simulator')
+    basis_gates = ['u1', 'u2', 'u3', 'cx']
+
+    rb_circs, xdata = rb.randomized_benchmarking_seq(**rb_opts)
+
+    # creating the noise
+    noise_model = NoiseModel()
+    zz_unitary = np.eye(4, dtype=complex)
+    zz_unitary[3, 3] = np.exp(0.5j * (np.pi / 5))
+    error = coherent_unitary_error(zz_unitary)
+    noise_model.add_nonlocal_quantum_error(error, basis_gates, [1], [1, 2])
+    noise_model.add_nonlocal_quantum_error(error, basis_gates, [2], [2, 1])
+
+    # Add depolarizing noise to the simulation
+    p_1qubit = 0.0005
+    p_2qubit = 0.005
+
+    noise_model.add_all_qubit_quantum_error(depolarizing_error(p_1qubit, 1), 'u2')
+    noise_model.add_all_qubit_quantum_error(depolarizing_error(2 * p_1qubit, 1), 'u3')
+    noise_model.add_all_qubit_quantum_error(depolarizing_error(p_2qubit, 2), 'cx')
+
+    results = []
+    for circuit in rb_circs:
+        results.append(qiskit.execute(circuit, backend=backend,
+                                      basis_gates=basis_gates,
+                                      shots=shots,
+                                      noise_model=noise_model,
+                                      seed_simulator=SEED).result())
+
+    return results, xdata
+
+
 def generate_fitter_data_1(results_file_path: str, expected_results_file_path: str):
     """
     Create rb circuits with depolarizing error and simulate them,
@@ -563,6 +659,72 @@ def generate_purity_data(results_file_path_purity: str,
         json.dump(coherent_expected_result, expected_results_file)
 
 
+def generate_correlated_fitter_data(results_1_file_path: str, expected_results_1_file_path: str,
+                                    results_2_file_path: str, expected_results_2_file_path: str):
+    """
+    Create correlated rb circuits with coherent weight 2 error and thermal relaxation error
+    and simulate them,
+    then write the results in a json file.
+    also creates fitter for the data results,
+    and also write the fitter results in another json file.
+    run 2 tests - with different rb_pattern
+
+    The simulation results file will contain a list of Result objects in a dictionary format.
+    The fitter data file will contain dictionary with the following keys:
+    - 'ydata', value is stored in the form of list of dictionaries with keys of:
+        - mean, list of integers
+        - std, list of integers
+    - 'alphas', value is stored in the form of dictionary of ints with keys of subsystems
+    - 'epsilon_opt_cost', int
+    - 'epsilon', value is stored in the form of dictionary of lists with keys of subsystems.
+        each list contains 2 items -
+        - epsilon, int
+        - est_err of that epsilon, int
+
+    Args:
+        results_1_file_path: path of the json file of the simulation results file of the first test
+        expected_results_1_file_path: path of the json file of the fitter results file of the
+         first test
+        results_2_file_path: path of the json file of the simulation results file  of the
+         second test
+        expected_results_2_file_path: path of the json file of the fitter results file of the
+         second test
+    """
+
+    rb_opts = {}
+    shots = 1024
+    rb_opts['nseeds'] = 20
+    rb_opts['rb_pattern'] = [[0], [1], [2], [3]]
+    rb_opts['length_vector'] = list(np.arange(1, 1200, 75))
+    rb_opts['rand_seed'] = SEED
+
+    results_file_paths = [results_1_file_path, results_2_file_path]
+    expected_results_paths = [expected_results_1_file_path, expected_results_2_file_path]
+
+    for test_ind, results_file_path in enumerate(results_file_paths):
+        if test_ind == 1:  # change the pattern for the second test
+            rb_opts['rb_pattern'] = [[0, 1], [2], [3]]
+            rb_results, xdata = rb_correlated_circuit_exec2(rb_opts, shots)
+        else:
+            rb_results, xdata = rb_correlated_circuit_exec1(rb_opts, shots)
+        save_results_as_json(rb_results, results_file_path)
+
+        # generate also the expected results of the fitter
+        rb_fit = CorrelatedRBFitter(rb_results, xdata, rb_opts['rb_pattern'])
+
+        ydata = rb_fit.ydata
+        alphas = rb_fit.fit_alphas
+        epsilon_opt_cost = rb_fit.fit_alphas_to_epsilon()
+        epsilon = rb_fit.fit_epsilon
+        # convert ndarray to list
+        ydata = convert_ndarray_to_list_in_data(ydata)
+
+        expected_result = {"ydata": ydata, "alphas": alphas, "epsilon_opt_cost": epsilon_opt_cost,
+                           "epsilon": epsilon}
+        with open(expected_results_paths[test_ind], "w") as expected_results_file:
+            json.dump(expected_result, expected_results_file)
+
+
 if __name__ == '__main__':
     DIRNAME = os.path.dirname(os.path.abspath(__file__))
     for rb_type in sys.argv[1:]:
@@ -592,5 +754,16 @@ def generate_purity_data(results_file_path_purity: str,
                                  os.path.join(DIRNAME, 'test_fitter_purity_expected_results.json'),
                                  os.path.join(DIRNAME,
                                               'test_fitter_coherent_purity_expected_results.json'))
+        elif rb_type == 'correlated':
+            generate_correlated_fitter_data(os.path.join(DIRNAME, 'test_fitter_correlated'
+                                                                  '_results_1.json'),
+                                            os.path.join(DIRNAME,
+                                                         'test_fitter_correlated'
+                                                         '_expected_results_1.json'),
+                                            os.path.join(DIRNAME, 'test_fitter_correlated'
+                                                                  '_results_2.json'),
+                                            os.path.join(DIRNAME,
+                                                         'test_fitter_correlated'
+                                                         '_expected_results_2.json'))
         else:
             print('Skipping unknown argument ' + rb_type)
diff --git a/test/rb/test_fitter_correlated_expected_results_1.json b/test/rb/test_fitter_correlated_expected_results_1.json
new file mode 100644
index 000000000..d558db203
--- /dev/null
+++ b/test/rb/test_fitter_correlated_expected_results_1.json
@@ -0,0 +1 @@
+{"ydata": [{"mean": [0.99169921875, 0.60732421875, 0.34013671875, 0.259375, 0.125390625, 0.09794921875, 0.0384765625, 0.0322265625, 0.06787109375, -0.006640625, 0.0212890625, -0.0029296875, 0.0140625, -0.0119140625, 0.0232421875, 0.0541015625], "std": [0.010942294568544784, 0.04135589416636768, 0.06042160364785041, 0.04297141326973593, 0.04490318904842673, 0.048672701857693595, 0.051745281501938356, 0.0632115863657835, 0.052210487282727064, 0.05291995701610061, 0.03544208885770782, 0.05674660075085858, 0.03835591411322298, 0.05222518930505871, 0.052708688767432184, 0.05252453911718462]}, {"mean": [0.9939453125, 0.61328125, 0.38173828125, 0.2595703125, 0.11875, 0.094921875, 0.0560546875, -0.00126953125, 0.03125, -0.0015625, -0.024609375, 0.05244140625, 0.0708984375, -0.0166015625, 0.0091796875, -0.00087890625], "std": [0.010259020302085075, 0.03905761688228607, 0.045815146334667455, 0.04369459731445818, 0.024671301054122075, 0.03382009503423963, 0.04308887682630337, 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0.9933125612483911, "0010": 0.9933755598592419, "0011": 0.9848353348101797, "0100": 0.9915529738078825, "0101": 0.9864176692664915, "0110": 0.9917754465489294, "0111": 0.9850413397193459, "1000": 0.9934816832003793, "1001": 0.9876188815754039, "1010": 0.9871566738221167, "1011": 0.9796165879241123, "1100": 0.9849709049462875, "1101": 0.9770985666859019, "1110": 0.9842940187663906, "1111": 0.9762312233880512}, "epsilon_opt_cost": 5.447432421763973e-06, "epsilon": {"0001": [0.006882561553188954, 0.00086280971336134], "0010": [0.0006767227501204149, 0.0008708993696575287], "0011": [1.7302400199945907e-07, 0.0012282375495283287], "0100": [0.0019265269360232856, 0.0008692668099981627], "0101": [1.4325112050218634e-05, 0.001227448584509522], "0110": [0.0062718517115123735, 0.0012216536934422817], "0111": [7.028800591974979e-07, 0.0017326031902867372], "1000": [0.006808357247048174, 0.0008629061618945474], "1001": [8.281280762876151e-08, 0.001224477918202573], "1010": [1.1771778215060745e-05, 0.001228265301116127], "1011": [6.315687174705149e-07, 0.0017310812368178804], "1100": [2.6458177129474165e-08, 0.0012275159960915803], "1101": [1.4272737417984465e-06, 0.001729997242675288], "1110": [5.740196628262818e-07, 0.0017326688376697894], "1111": [1.1600176648882382e-06, 0.002366407390404082]}}
\ No newline at end of file
diff --git a/test/rb/test_fitter_correlated_expected_results_2.json b/test/rb/test_fitter_correlated_expected_results_2.json
new file mode 100644
index 000000000..d06134550
--- /dev/null
+++ b/test/rb/test_fitter_correlated_expected_results_2.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/test/rb/test_fitter_correlated_results_1.json b/test/rb/test_fitter_correlated_results_1.json
new file mode 100644
index 000000000..955f33eda
--- /dev/null
+++ b/test/rb/test_fitter_correlated_results_1.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/test/rb/test_fitter_correlated_results_2.json b/test/rb/test_fitter_correlated_results_2.json
new file mode 100644
index 000000000..cf5e079c0
--- /dev/null
+++ b/test/rb/test_fitter_correlated_results_2.json
@@ -0,0 +1 @@
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"0x4": 31, "0x5": 33, "0x6": 89, "0x7": 75, "0x8": 43, "0x9": 42, "0xa": 31, "0xb": 40, "0xc": 5, "0xd": 9, "0xe": 25, "0xf": 21}}, "meas_level": 2, "header": {"clbit_labels": [["cr", 0], ["cr", 1], ["cr", 2], ["cr", 3]], "creg_sizes": [["cr", 4]], "global_phase": 0, "memory_slots": 4, "n_qubits": 4, "name": "rb_length_13_seed_19", "qreg_sizes": [["qr", 4]], "qubit_labels": [["qr", 0], ["qr", 1], ["qr", 2], ["qr", 3]]}, "status": "DONE", "seed_simulator": 27511, "time_taken": 0.186071793, "metadata": {"fusion": {"applied": false, "cost_factor": 1.8, "max_fused_qubits": 2, "method": "superop", "threshold": 10}, "measure_sampling": true, "method": "density_matrix", "parallel_shots": 1, "parallel_state_update": 8}}, {"shots": 1024, "success": true, "data": {"counts": {"0x0": 149, "0x1": 169, "0x2": 103, "0x3": 95, "0x4": 37, "0x5": 47, "0x6": 103, "0x7": 103, "0x8": 51, "0x9": 38, "0xa": 26, "0xb": 27, "0xc": 9, "0xd": 11, "0xe": 29, "0xf": 27}}, "meas_level": 2, "header": {"clbit_labels": [["cr", 0], ["cr", 1], ["cr", 2], ["cr", 3]], "creg_sizes": [["cr", 4]], "global_phase": 0, "memory_slots": 4, "n_qubits": 4, "name": "rb_length_14_seed_19", "qreg_sizes": [["qr", 4]], "qubit_labels": [["qr", 0], ["qr", 1], ["qr", 2], ["qr", 3]]}, "status": "DONE", "seed_simulator": 29624, "time_taken": 0.19929565200000002, "metadata": {"fusion": {"applied": false, "cost_factor": 1.8, "max_fused_qubits": 2, "method": "superop", "threshold": 10}, "measure_sampling": true, "method": "density_matrix", "parallel_shots": 1, "parallel_state_update": 8}}, {"shots": 1024, "success": true, "data": {"counts": {"0x0": 154, "0x1": 190, "0x2": 143, "0x3": 151, "0x4": 43, "0x5": 26, "0x6": 45, "0x7": 55, "0x8": 49, "0x9": 40, "0xa": 43, "0xb": 31, "0xc": 14, "0xd": 15, "0xe": 16, "0xf": 9}}, "meas_level": 2, "header": {"clbit_labels": [["cr", 0], ["cr", 1], ["cr", 2], ["cr", 3]], "creg_sizes": [["cr", 4]], "global_phase": 0, "memory_slots": 4, "n_qubits": 4, "name": "rb_length_15_seed_19", "qreg_sizes": [["qr", 4]], "qubit_labels": [["qr", 0], ["qr", 1], ["qr", 2], ["qr", 3]]}, "status": "DONE", "seed_simulator": 31737, "time_taken": 0.20575469300000002, "metadata": {"fusion": {"applied": false, "cost_factor": 1.8, "max_fused_qubits": 2, "method": "superop", "threshold": 10}, "measure_sampling": true, "method": "density_matrix", "parallel_shots": 1, "parallel_state_update": 8}}], "date": "2020-10-11T16:00:32.199679", "status": "COMPLETED", "header": {"backend_name": "qasm_simulator", "backend_version": "0.6.1"}, "metadata": {"max_memory_mb": 8192, "omp_enabled": true, "parallel_experiments": 1, "time_taken": 1.971580705}, "time_taken": 2.4176549911499023}]
\ No newline at end of file
diff --git a/test/rb/test_fitters.py b/test/rb/test_fitters.py
index 2b835befe..acdd63744 100644
--- a/test/rb/test_fitters.py
+++ b/test/rb/test_fitters.py
@@ -26,7 +26,7 @@
 import numpy as np
 
 from qiskit.ignis.verification.randomized_benchmarking import \
-    RBFitter, InterleavedRBFitter, PurityRBFitter, CNOTDihedralRBFitter
+    RBFitter, InterleavedRBFitter, PurityRBFitter, CNOTDihedralRBFitter, CorrelatedRBFitter
 
 
 class TestFitters(unittest.TestCase):
@@ -42,7 +42,7 @@ def compare_results_and_excpected(self, data, expected_data, tst_index):
                                       + str(tst_index))
                 else:
                     # check if the zip function is needed
-                    if isinstance(data[i][key], np.ndarray):
+                    if isinstance(data[i][key], (list, np.ndarray)):
                         self.assertTrue(all(np.isclose(a, b) for a, b in
                                             zip(data[i][key], expect_data[key])),
                                         'Incorrect ' + str(key) + ' in test no. ' + str(tst_index))
@@ -271,6 +271,77 @@ def test_cnotdihedral_fitters(self):
                                                tst_cnotdihedral_expected_results['joint_fit'],
                                                tst_index)
 
+    def test_correlated_fitters(self):
+        """ Test the correlated rb fitters """
+        # Use json results files
+        tests_settings = [
+            {
+                'rb_opts': {
+                    'xdata': np.array([[1, 76, 151, 226, 301, 376, 451, 526, 601, 676, 751,
+                                        826, 901, 976, 1051, 1126],
+                                       [1, 76, 151, 226, 301, 376, 451, 526, 601, 676, 751,
+                                        826, 901, 976, 1051, 1126],
+                                       [1, 76, 151, 226, 301, 376, 451, 526, 601, 676, 751,
+                                        826, 901, 976, 1051, 1126]]),
+                    'rb_pattern': [[0], [1], [2], [3]],
+                    'shots': 1024},
+                'correlated_results_file':
+                    os.path.join(os.path.dirname(__file__),
+                                 'test_fitter_correlated_results_1.json'),
+                'correlated_expected_results_file':
+                    os.path.join(os.path.dirname(__file__),
+                                 'test_fitter_correlated_expected_results_1.json')
+            },
+            {
+                'rb_opts': {
+                    'xdata': np.array([[1, 76, 151, 226, 301, 376, 451, 526, 601, 676, 751,
+                                        826, 901, 976, 1051, 1126],
+                                       [1, 76, 151, 226, 301, 376, 451, 526, 601, 676, 751,
+                                        826, 901, 976, 1051, 1126],
+                                       [1, 76, 151, 226, 301, 376, 451, 526, 601, 676, 751,
+                                        826, 901, 976, 1051, 1126]]),
+                    'rb_pattern': [[0, 1], [2], [3]],
+                    'shots': 1024},
+                'correlated_results_file':
+                    os.path.join(os.path.dirname(__file__),
+                                 'test_fitter_correlated_results_2.json'),
+                'correlated_expected_results_file':
+                    os.path.join(os.path.dirname(__file__),
+                                 'test_fitter_correlated_expected_results_2.json')
+            }
+        ]
+
+        for tst_index, tst_settings in enumerate(tests_settings):
+            correlated_result = load_results_from_json(
+                tst_settings['correlated_results_file'])
+            with open(tst_settings['correlated_expected_results_file'], "r") \
+                    as expected_results_file:
+                tst_correlated_expected_results = json.load(expected_results_file)
+
+            # CorrelatedRBFitter class
+            rb_fit = CorrelatedRBFitter(
+                correlated_result,
+                tst_settings['rb_opts']['xdata'],
+                tst_settings['rb_opts']['rb_pattern'])
+
+            ydata = rb_fit.ydata
+            alphas = rb_fit.fit_alphas
+            epsilon_opt_cost = rb_fit.fit_alphas_to_epsilon()
+            epsilon = rb_fit.fit_epsilon
+
+            self.compare_results_and_excpected(ydata,
+                                               tst_correlated_expected_results['ydata'],
+                                               tst_index)
+            self.compare_results_and_excpected([alphas],
+                                               [tst_correlated_expected_results['alphas']],
+                                               tst_index)
+            self.assertTrue(np.isclose(epsilon_opt_cost,
+                                       tst_correlated_expected_results['epsilon_opt_cost']),
+                            'Incorrect epsilon_opt_cost in test no. ' + str(tst_index))
+            self.compare_results_and_excpected([epsilon],
+                                               [tst_correlated_expected_results['epsilon']],
+                                               tst_index)
+
 
 if __name__ == '__main__':
     unittest.main()