Merge branch 'qutip:master' into heom_mauro_inputoutpuit

.github/workflows/build.yml

@@ -105,10 +105,11 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
+        # between 13 and 14, mac changed from intel chip to apple silicon
+        os: [ubuntu-latest, windows-latest, macos-13, macos-latest]
     env:
-      # Set up wheels matrix.  This is CPython 3.10--3.12 for all OS targets.
-      CIBW_BUILD: "cp3{10,11,12}-*"
+      # Set up wheels matrix.  This is CPython 3.10--3.13 for all OS targets.
+      CIBW_BUILD: "cp3{10,11,12,13}-*"
       # Numpy and SciPy do not supply wheels for i686 or win32 for
       # Python 3.10+, so we skip those:
       CIBW_SKIP: "*-musllinux* *-manylinux_i686 *-win32"
@@ -126,8 +127,8 @@ jobs:
       - name: Install cibuildwheel
         run: |
           # cibuildwheel does the heavy lifting for us. Tested on
-          # 2.19, but should be fine at least up to any minor new release.
-          python -m pip install 'cibuildwheel==2.19.*'
+          # 2.22, but should be fine at least up to any minor new release.
+          python -m pip install 'cibuildwheel==2.22.*'
 
       - name: Build wheels
         shell: bash
@@ -163,7 +164,7 @@ jobs:
       - name: Check wheels
         run: |
           ls -R wheels
-          if ! [[ $(ls wheels/*.whl | wc -l) == 9 ]]; then exit 1; fi
+          if ! [[ $(ls wheels/*.whl | wc -l) == 16 ]]; then exit 1; fi
           if ! ls wheels/*.tar.gz 1> /dev/null 2>&1; then exit 1; fi
           if ! ls wheels/*.zip 1> /dev/null 2>&1; then exit 1; fi
 

.github/workflows/tests.yml

@@ -87,8 +87,6 @@ jobs:
           # Mac
           # Mac has issues with MKL since september 2022.
           - case-name: macos
-            # setup-miniconda not compatible with macos-latest presently.
-            # https://github.com/conda-incubator/setup-miniconda/issues/344
             os: macos-latest
             python-version: "3.12"
             numpy-build: ">=2.0.0"
@@ -97,7 +95,7 @@ jobs:
             nomkl: 1
 
           - case-name: macos - numpy fallback
-            os: macos-latest
+            os: macos-13  # Test on intel cpus
             python-version: "3.11"
             numpy-build: ">=2.0.0"
             numpy-requirement: ">=1.25,<1.26"
@@ -145,6 +143,7 @@ jobs:
           python -m pip install loky tqdm mpmath  # extras
           python -m pip install "coverage${{ matrix.coverage-requirement }}" chardet
           python -m pip install pytest-cov coveralls pytest-fail-slow
+          python -m pip install setuptools
 
           if [[ "${{ matrix.pypi }}" ]]; then
             pip install "numpy${{ matrix.numpy-build }}"

VERSION

@@ -1 +1 @@
-5.1.0.dev
+5.2.0.dev

doc/changelog.rst

@@ -6,6 +6,79 @@ Change Log
 
 .. towncrier release notes start
 
+QuTiP 5.1.0 (2024-12-12)
+========================
+
+Features
+--------
+
+- It adds odd parity support to HEOM's fermionic solver (#2261, by Gerardo Jose Suarez)
+- Create `SMESolver.run_from_experiment`, which allows to run stochastic evolution from know noise or measurements. (#2318)
+- Add types hints. (#2327, #2473)
+- Weighted trajectories in trajectory solvers (enables improved sampling for nm_mcsolve) (#2369, by Paul Menczel)
+- Updated `qutip.core.metrics.dnorm` to have an efficient speedup when finding the difference of two unitaries. We use a result on page 18 of
+  D. Aharonov, A. Kitaev, and N. Nisan, (1998). (#2416, by owenagnel)
+- Allow mixed initial conditions for mcsolve and nm_mcsolve. (#2437, by Paul Menczel)
+- Add support for `jit` and `grad` in qutip.core.metrics (#2461, by Rochisha Agarwal)
+- Allow merging results from stochastic solvers. (#2474)
+- Support measurement statistics for `jax` and `jaxdia` dtypes (#2493, by Rochisha Agarwal)
+- Enable mcsolve with jax.grad using numpy_backend (#2499, by Rochisha Agarwal)
+- Add propagator method to steadystate (#2508)
+- Introduces the qutip.core.environment module, which contains classes that characterize bosonic and fermionic thermal environments. (#2534, by Gerardo Jose Suarez)
+- Implements a `einsum` function for Qobj dimensions (Evaluates the Einstein summation convention on the operands.) (#2545, by Franco Mayo)
+- Wave function calculations have been sped up with a Cython implementation.
+  It optimizes the update method of the HarmonicOscillatorWaveFunction class in distribution.py. (#2553, by Matheus Gomes Cordeiro)
+- Speed up `kraus_to_super` by adding a `sparse` option. (#2569, by Sola85)
+
+
+Bug Fixes
+---------
+
+- Fix a dimension problem for the argument color of Bloch.add_states
+  Clean-up of the code in  Bloch.add_state
+  Add Bloch.add_arc and Bloch.add_line in the guide on Bloch class (#2445, by PositroniumJS)
+- Fix HTMLProgressBar display (#2475)
+- Make expm, cosm, sinm work with jax. (#2484, by Rochisha Agarwal)
+- Fix stochastic solver step method (#2491)
+- `clip` gives deprecation warning, that might be a problem in the future. Hence switch to `where` (#2507, by Rochisha Agarwal)
+- Fix brmesolve detection of contant vs time-dependent system. (#2530)
+- `propagator` now accepts list format `c_ops` like `mesolve` does. (#2532)
+- Fix compatibility issue with matplotlib>=3.9 in matrix_histogram (#2544, by Andreas Maeder)
+- Resolve incompatibility of TwoModeQuadratureCorrelation class (#2548, by quantum-menace)
+- Fix sparse eigen solver issue with many degenerate eigen values. (#2586)
+- Fix getting tensor permutation for uneven super operators. (#2561)
+
+
+Documentation
+-------------
+
+- Improve guide-settings page. (#2403)
+- Tidy up formatting of type aliases in the api documentation (#2436, by Paul Menczel)
+- Update documentation
+  - Update contributors
+  - Improve apidoc readability (#2523)
+- Fix error in simdiag docstring (#2585, by Sola85)
+
+
+Miscellaneous
+-------------
+
+- Add auto_real_casting options. (#2329)
+- Add dispatcher for sqrtm (#2453, by Rochisha Agarwal)
+- Make `e_ops`, `args` and `options` keyword only.
+  Solver were inconsistent with `e_ops` usually following `c_ops` but sometime
+  preceding it. Setting it as keyword only remove the need to memorize the
+  signature of each solver. (#2489)
+- Introduces a new `NumpyBackend `class that enables dynamic selection of the numpy_backend used in `qutip`.
+  The class facilitates switching between different numpy implementations ( `numpy` and `jax.numpy` mainly ) based on the configuration specified in `settings.core`. (#2490, by Rochisha Agarwal)
+- Improve mkl lookup function. (#2497)
+- Deterministic trajectories are not counted in ``ntraj``. (#2502)
+- Allow tests to be executed multiple times in one Python session (#2538, by Zhang Maiyun)
+- Improve performance of qutip.Qobj by using static numpy version check (#2557, by Pieter Eendebak)
+- Fix towncrier check (#2542)
+
+
+
 QuTiP 5.0.4 (2024-08-30)
 ========================
 

qutip/core/data/dense.pyx

@@ -27,6 +27,13 @@ cdef extern from *:
     void PyDataMem_FREE(void *ptr)
 
 
+@cython.overflowcheck(True)
+cdef size_t _mul_mem_checked(size_t a, size_t b, size_t c=0):
+    if c != 0:
+        return a * b * c
+    return a * b
+
+
 # Creation functions like 'identity' and 'from_csr' aren't exported in __all__
 # to avoid naming clashes with other type modules.
 __all__ = [
@@ -122,7 +129,9 @@ cdef class Dense(base.Data):
         low-level C code).
         """
         cdef Dense out = Dense.__new__(Dense)
-        cdef size_t size = self.shape[0]*self.shape[1]*sizeof(double complex)
+        cdef size_t size = (
+            _mul_mem_checked(self.shape[0], self.shape[1], sizeof(double complex))
+        )
         cdef double complex *ptr = <double complex *> PyDataMem_NEW(size)
         if not ptr:
             raise MemoryError(
@@ -169,7 +178,9 @@ cdef class Dense(base.Data):
         dimensions.  This is not a view onto the data, and changes to new array
         will not affect the original data structure.
         """
-        cdef size_t size = self.shape[0]*self.shape[1]*sizeof(double complex)
+        cdef size_t size = (
+          _mul_mem_checked(self.shape[0], self.shape[1], sizeof(double complex))
+        )
         cdef double complex *ptr = <double complex *> PyDataMem_NEW(size)
         if not ptr:
             raise MemoryError(
@@ -257,7 +268,9 @@ cpdef Dense empty(base.idxint rows, base.idxint cols, bint fortran=True):
     """
     cdef Dense out = Dense.__new__(Dense)
     out.shape = (rows, cols)
-    out.data = <double complex *> PyDataMem_NEW(rows * cols * sizeof(double complex))
+    out.data = <double complex *> PyDataMem_NEW(
+        _mul_mem_checked(rows, cols, sizeof(double complex))
+    )
     if not out.data:
         raise MemoryError(
             "Could not allocate memory to create an empty "
@@ -282,7 +295,9 @@ cpdef Dense zeros(base.idxint rows, base.idxint cols, bint fortran=True):
     cdef Dense out = Dense.__new__(Dense)
     out.shape = (rows, cols)
     out.data =\
-        <double complex *> PyDataMem_NEW_ZEROED(rows * cols, sizeof(double complex))
+        <double complex *> PyDataMem_NEW_ZEROED(
+            _mul_mem_checked(rows, cols), sizeof(double complex)
+        )
     if not out.data:
         raise MemoryError(
             "Could not allocate memory to create a zero "
@@ -311,8 +326,9 @@ cpdef Dense from_csr(CSR matrix, bint fortran=False):
     cdef Dense out = Dense.__new__(Dense)
     out.shape = matrix.shape
     out.data = (
-        <double complex *>
-        PyDataMem_NEW_ZEROED(out.shape[0]*out.shape[1], sizeof(double complex))
+        <double complex *> PyDataMem_NEW_ZEROED(
+            _mul_mem_checked(out.shape[0], out.shape[1]), sizeof(double complex)
+        )
     )
     if not out.data:
         raise MemoryError(

qutip/core/data/eigen.py

@@ -239,8 +239,9 @@ def eigs_csr(data, /, isherm=None, vecs=True, sort='low', eigvals=0,
 
     if vecs and isherm:
         i = 0
+        degen_tol = (2 * tol or 1e-15 * data.shape[0])
         while i < len(evals):
-            num_degen = np.sum(np.abs(evals - evals[i]) < (2 * tol or 1e-14))
+            num_degen = np.sum(np.abs(evals[i:] - evals[i]) < degen_tol)
             # orthogonalize vectors 1 .. k with respect to the first, then
             # 2 .. k with respect to the second, and so on. Relies on both the
             # order of each pair and the ordering of pairs returned by

qutip/core/dimensions.py

@@ -250,7 +250,7 @@ def dims_to_tensor_shape(dims):
     tensor_shape : tuple
         NumPy shape of the corresponding tensor.
     """
-    perm = dims_to_tensor_perm(dims)
+    perm = np.argsort(dims_to_tensor_perm(dims))
     dims = flatten(dims)
     return tuple(map(partial(getitem, dims), perm))
 
@@ -936,10 +936,10 @@ def _get_tensor_perm(self):
         # dims_to_tensor_perm
         stepl = self.to_.step()
         stepr = self.from_.step()
-        return list(np.concatenate([
+        return list(np.argsort(np.concatenate([
             np.argsort(stepl)[::-1],
             np.argsort(stepr)[::-1] + len(stepl)
-        ]))
+        ])))
 
     def remove(self, idx: int | list[int]) -> "Dimensions":
         """

qutip/simdiag.py

@@ -41,8 +41,13 @@ def _degen(tol, vecs, ops, i=0):
     return vecs_new
 
 
-def simdiag(ops, evals: bool = True, *,
-            tol: float = 1e-14, safe_mode: bool = True):
+def simdiag(
+    ops,
+    evals: bool = True, *,
+    tol: float = 1e-14,
+    safe_mode: bool = True,
+    use_dense_solver: bool = True,
+):
     """Simultaneous diagonalization of commuting Hermitian matrices.
 
     Parameters
@@ -58,15 +63,20 @@ def simdiag(ops, evals: bool = True, *,
     tol : float, default: 1e-14
         Tolerance for detecting degenerate eigenstates.
 
-    safe_mode : bool, default:  True
+    safe_mode : bool, default: True
         Whether to check that all ops are Hermitian and commuting. If set to
         ``False`` and operators are not commuting, the eigenvectors returned
         will often be eigenvectors of only the first operator.
 
+    use_dense_solver: bool, default: True
+        Whether to force use of numpy dense eigen solver. When ``False``
+        sparse operators will use scipy sparse eigen solver which is not
+        appropriate for this use.
+
     Returns
     -------
     eigs : tuple
-        Tuple of arrays representing eigvecs and eigvals of quantum objects
+        Tuple of arrays representing eigvals and eigvecs of quantum objects
         corresponding to simultaneous eigenvectors and eigenvalues for each
         operator.
 
@@ -89,7 +99,10 @@ def simdiag(ops, evals: bool = True, *,
             if (A * B - B * A).norm() / (A * B).norm() > tol:
                 raise TypeError('Matrices must commute.')
 
-    # TODO: rewrite using Data object
+    if use_dense_solver:
+        # Do not use sparse eigen solver.
+        ops = [op.to("Dense") for op in ops]
+
     eigvals, eigvecs = _data.eigs(ops[0].data, True, True)
     eigvecs = eigvecs.to_array()