Refactor functions in matrixtools.py

pygsti/extras/interpygate/__init__.py

@@ -10,3 +10,15 @@
 
 from .core import PhysicalProcess, InterpolatedDenseOp, InterpolatedOpFactory
 from .process_tomography import vec, unvec, run_process_tomography
+
+# Note from Riley on September, 2024:
+#
+#   vec is deprecated, and shouldn't be called anywhere in the codebase.
+#
+#   unvec is deprecated and replaced with unvec_square; the latter function
+#   isn't imported here because we don't want people to access it just from
+#   the pygsti.extras.interpygate namespace.
+#
+#   Ideally we'd remove vec and unvec from the pygsti.extras.interpygate namespace
+#   and only have them available in pygsti.extras.interpygate.process_tomography.
+#

pygsti/extras/interpygate/process_tomography.py

@@ -7,6 +7,7 @@
 import numpy.linalg as _lin
 
 from pygsti.tools.basistools import change_basis
+from pygsti.tools.legacytools import deprecate
 
 
 #Helper functions
@@ -15,8 +16,11 @@ def multi_kron(*a):
     return reduce(_np.kron, a)
 
 
+@deprecate("Calls to this function should be replaced with in-lined code: matrix.reshape((matrix.size, 1), 'F')")
 def vec(matrix):
-    """A function that vectorizes a matrix.
+    """
+    Returns an explicit column-vector representation of a square matrix, obtained by reading
+    from the square matrix in column-major order.
 
     Args:
         matrix (list,numpy.ndarray): NxN matrix
@@ -30,11 +34,12 @@ def vec(matrix):
     """
     matrix = _np.array(matrix)
     if matrix.shape == (len(matrix), len(matrix)):
-        return _np.array([_np.concatenate(_np.array(matrix).T)]).T
+        return matrix.reshape(shape=(matrix.size, 1), order='F')
     else:
         raise ValueError('The input matrix must be square.')
 
 
+@deprecate("Calls to this function should be replaced by unvec_square(vectorized, 'F')")
 def unvec(vectorized):
     """A function that vectorizes a process in the basis of matrix units, sorted first
     by column, then row.
@@ -49,13 +54,42 @@ def unvec(vectorized):
         ValueError: If the length of the input is not a perfect square
 
     """
-    vectorized = _np.array(vectorized)
-    length = int(_np.sqrt(max(vectorized.shape)))
-    if len(vectorized) == length ** 2:
-        return _np.reshape(vectorized, [length, length]).T
+    return unvec_square(vectorized, order='F')
+
+
+def unvec_square(vectorized, order):
+    """
+    Takes a vector whose length is a perfect square, and returns a square matrix
+    representation by reading from the vectors entries to define the matrix in 
+    column-major order (order='F') or row-major order (order='C').
+
+    Args:
+        vectorized: array-like, where np.array(vectorized).size is a perfect square.
+        order: 'F' or 'C'
+
+    Returns:
+        numpy.ndarray: NxN dimensional array
+
+    Raises:
+        ValueError: If the length of the input is not a perfect square.
+
+    """
+    assert order == 'F' or order == 'C'
+    if not isinstance(vectorized, _np.ndarray):
+        vectorized = _np.array(vectorized)
+
+    if vectorized.ndim == 2:
+        assert min(vectorized.shape) == 1
+        vectorized = vectorized.ravel()
+    elif vectorized.ndim > 2:
+        raise ValueError('vectorized.ndim must be <= 2.')
+
+    n = int(_np.sqrt(max(vectorized.shape)))
+    if len(vectorized) == n ** 2:
+        return vectorized.reshape(shape=(n, n), order=order)
     else:
-        raise ValueError(
-            'The input vector length must be a perfect square, but this input has length %d.' % len(vectorized))
+        msg = 'The input vector length must be a perfect square, but this input has length %d.' % len(vectorized)
+        raise ValueError(msg)
 
 
 def split(n, a):
@@ -129,7 +163,7 @@ def run_process_tomography(state_to_density_matrix_fn, n_qubits=1, comm=None,
     states = _itertools.product(one_qubit_states, repeat=n_qubits)
     states = [multi_kron(*state) for state in states]
     in_density_matrices = [_np.outer(state, state.conj()) for state in states]
-    in_states = _np.column_stack(list([vec(rho) for rho in in_density_matrices]))
+    in_states = _np.column_stack(list([rho.ravel(order='F') for rho in in_density_matrices]))
     my_states = split(size, states)[rank]
     if verbose:
         print("Process %d of %d evaluating %d input states." % (rank, size, len(my_states)))
@@ -150,7 +184,7 @@ def run_process_tomography(state_to_density_matrix_fn, n_qubits=1, comm=None,
         out_density_matrices = _np.array([y for x in gathered_out_density_matrices for y in x])
         # Sort the list by time
         out_density_matrices = _np.transpose(out_density_matrices, [1, 0, 2, 3])
-        out_states = [_np.column_stack(list([vec(rho) for rho in density_matrices_at_time]))
+        out_states = [_np.column_stack(list([rho.ravel(order='F') for rho in density_matrices_at_time]))
                       for density_matrices_at_time in out_density_matrices]
         process_matrices = [_np.dot(out_states_at_time, _lin.inv(in_states)) for out_states_at_time in out_states]
         process_matrices = [change_basis(process_matrix_at_time, 'col', basis)

pygsti/modelmembers/operations/experrorgenop.py

@@ -699,9 +699,9 @@ def spam_transform_inplace(self, s, typ):
 
             #just act on postfactor and Lindbladian exponent:
             if typ == "prep":
-                mx = _mt.safe_dot(Uinv, mx)
+                mx = Uinv @ mx
             else:
-                mx = _mt.safe_dot(mx, U)
+                mx = mx @ U
             self.set_dense(mx)  # calls _update_rep() and sets dirty flag
         else:
             raise ValueError("Invalid transform for this LindbladErrorgen: type %s"

pygsti/modelmembers/operations/fullunitaryop.py

@@ -200,7 +200,7 @@ def transform_inplace(self, s):
             Uinv = s.transform_matrix_inverse
 
             my_superop_mx = _ot.unitary_to_superop(self._ptr, self._basis)
-            my_superop_mx = _mt.safe_dot(Uinv, _mt.safe_dot(my_superop_mx, U))
+            my_superop_mx = Uinv @ (my_superop_mx @ U)
 
             self._ptr[:, :] = _ot.superop_to_unitary(my_superop_mx, self._basis)
             self._ptr_has_changed()
@@ -250,9 +250,9 @@ def spam_transform_inplace(self, s, typ):
 
             #Note: this code may need to be tweaked to work with sparse matrices
             if typ == "prep":
-                my_superop_mx = _mt.safe_dot(Uinv, my_superop_mx)
+                my_superop_mx = Uinv @ my_superop_mx
             else:
-                my_superop_mx = _mt.safe_dot(my_superop_mx, U)
+                my_superop_mx = my_superop_mx @ U
 
             self._ptr[:, :] = _ot.superop_to_unitary(my_superop_mx, self._basis)
             self._ptr_has_changed()

pygsti/modelmembers/operations/lindbladcoefficients.py

@@ -195,7 +195,7 @@ def create_lindblad_term_superoperators(self, mx_basis='pp', sparse="auto", incl
                 if sparse:
                     #Note: complex OK here sometimes, as only linear combos of "other" gens
                     # (like (i,j) + (j,i) terms) need to be real.
-                    superops = [_mt.safe_dot(leftTrans, _mt.safe_dot(mx, rightTrans)) for mx in superops]
+                    superops = [leftTrans @ (mx @ rightTrans) for mx in superops]
                     for mx in superops: mx.sort_indices()
                 else:
                     #superops = _np.einsum("ik,akl,lj->aij", leftTrans, superops, rightTrans)

pygsti/modelmembers/operations/lindbladerrorgen.py

@@ -497,140 +497,7 @@ def __init__(self, lindblad_coefficient_blocks, lindblad_basis='auto', mx_basis=
         self._paramlbls = _np.array(list(_itertools.chain.from_iterable(
             [blk.param_labels for blk in self.coefficient_blocks])), dtype=object)
         assert(self._onenorm_upbound is not None)  # _update_rep should set this
-        #Done with __init__(...)
-
-    #def _init_generators(self, dim):
-    #    #assumes self.dim, self.ham_basis, self.other_basis, and self.matrix_basis are setup...
-    #    sparse_bases = bool(self._rep_type == 'sparse superop')
-    #
-    #    #HERE TODO - need to update this / MOVE to block class?
-    #    #use caching to increase performance - cache based on all the self.XXX members utilized by this fn
-    #    cache_key = (self._rep_type, self.matrix_basis, self.ham_basis, self.other_basis, self.parameterization)
-    #    #print("cache key = ",self._rep_type, (self.matrix_basis.name, self.matrix_basis.dim),
-    #    #      (self.ham_basis.name, self.ham_basis.dim), (self.other_basis.name, self.other_basis.dim),
-    #    #      str(self.parameterization))
-    #
-    #    if cache_key not in self._generators_cache:
-    #
-    #        d = int(round(_np.sqrt(dim)))
-    #        assert(d * d == dim), "Errorgen dim must be a perfect square"
-    #
-    #        # Get basis transfer matrix
-    #        mxBasisToStd = self.matrix_basis.create_transform_matrix(
-    #            _BuiltinBasis("std", self.matrix_basis.dim, sparse_bases))
-    #        # use BuiltinBasis("std") instead of just "std" in case matrix_basis is a TensorProdBasis
-    #        leftTrans = _spsl.inv(mxBasisToStd.tocsc()).tocsr() if _sps.issparse(mxBasisToStd) \
-    #            else _np.linalg.inv(mxBasisToStd)
-    #        rightTrans = mxBasisToStd
-    #
-    #        hamBasisMxs = self.ham_basis.elements
-    #        otherBasisMxs = self.other_basis.elements
-    #
-    #        hamGens, otherGens = _ot.lindblad_error_generators(
-    #            hamBasisMxs, otherBasisMxs, normalize=False,
-    #            other_mode=self.parameterization.nonham_mode)  # in std basis
-    #
-    #        # Note: lindblad_error_generators will return sparse generators when
-    #        #  given a sparse basis (or basis matrices)
-    #
-    #        if hamGens is not None:
-    #            bsH = len(hamGens) + 1  # projection-basis size (not nec. == dim)
-    #            _ot._assert_shape(hamGens, (bsH - 1, dim, dim), sparse_bases)
-    #
-    #            # apply basis change now, so we don't need to do so repeatedly later
-    #            if sparse_bases:
-    #                hamGens = [_mt.safe_real(_mt.safe_dot(leftTrans, _mt.safe_dot(mx, rightTrans)),
-    #                                         inplace=True, check=True) for mx in hamGens]
-    #                for mx in hamGens: mx.sort_indices()
-    #                # for faster addition ops in _construct_errgen_matrix
-    #            else:
-    #                #hamGens = _np.einsum("ik,akl,lj->aij", leftTrans, hamGens, rightTrans)
-    #                hamGens = _np.transpose(_np.tensordot(
-    #                    _np.tensordot(leftTrans, hamGens, (1, 1)), rightTrans, (2, 0)), (1, 0, 2))
-    #        else:
-    #            bsH = 0
-    #        assert(bsH == self.ham_basis_size)
-    #
-    #        if otherGens is not None:
-    #
-    #            if self.parameterization.nonham_mode == "diagonal":
-    #                bsO = len(otherGens) + 1  # projection-basis size (not nec. == dim)
-    #                _ot._assert_shape(otherGens, (bsO - 1, dim, dim), sparse_bases)
-    #
-    #                # apply basis change now, so we don't need to do so repeatedly later
-    #                if sparse_bases:
-    #                    otherGens = [_mt.safe_real(_mt.safe_dot(leftTrans, _mt.safe_dot(mx, rightTrans)),
-    #                                               inplace=True, check=True) for mx in otherGens]
-    #                    for mx in otherGens: mx.sort_indices()
-    #                    # for faster addition ops in _construct_errgen_matrix
-    #                else:
-    #                    #otherGens = _np.einsum("ik,akl,lj->aij", leftTrans, otherGens, rightTrans)
-    #                    otherGens = _np.transpose(_np.tensordot(
-    #                        _np.tensordot(leftTrans, otherGens, (1, 1)), rightTrans, (2, 0)), (1, 0, 2))
-    #
-    #            elif self.parameterization.nonham_mode == "diag_affine":
-    #                # projection-basis size (not nec. == dim) [~shape[1] but works for lists too]
-    #                bsO = len(otherGens[0]) + 1
-    #                _ot._assert_shape(otherGens, (2, bsO - 1, dim, dim), sparse_bases)
-    #
-    #                # apply basis change now, so we don't need to do so repeatedly later
-    #                if sparse_bases:
-    #                    otherGens = [[_mt.safe_dot(leftTrans, _mt.safe_dot(mx, rightTrans))
-    #                                  for mx in mxRow] for mxRow in otherGens]
-    #
-    #                    for mxRow in otherGens:
-    #                        for mx in mxRow: mx.sort_indices()
-    #                        # for faster addition ops in _construct_errgen_matrix
-    #                else:
-    #                    #otherGens = _np.einsum("ik,abkl,lj->abij", leftTrans,
-    #                    #                          otherGens, rightTrans)
-    #                    otherGens = _np.transpose(_np.tensordot(
-    #                        _np.tensordot(leftTrans, otherGens, (1, 2)), rightTrans, (3, 0)), (1, 2, 0, 3))
-    #
-    #            else:
-    #                bsO = len(otherGens) + 1  # projection-basis size (not nec. == dim)
-    #                _ot._assert_shape(otherGens, (bsO - 1, bsO - 1, dim, dim), sparse_bases)
-    #
-    #                # apply basis change now, so we don't need to do so repeatedly later
-    #                if sparse_bases:
-    #                    otherGens = [[_mt.safe_dot(leftTrans, _mt.safe_dot(mx, rightTrans))
-    #                                  for mx in mxRow] for mxRow in otherGens]
-    #                    #Note: complex OK here, as only linear combos of otherGens (like (i,j) + (j,i)
-    #                    # terms) need to be real
-    #
-    #                    for mxRow in otherGens:
-    #                        for mx in mxRow: mx.sort_indices()
-    #                        # for faster addition ops in _construct_errgen_matrix
-    #                else:
-    #                    #otherGens = _np.einsum("ik,abkl,lj->abij", leftTrans,
-    #                    #                            otherGens, rightTrans)
-    #                    otherGens = _np.transpose(_np.tensordot(
-    #                        _np.tensordot(leftTrans, otherGens, (1, 2)), rightTrans, (3, 0)), (1, 2, 0, 3))
-    #
-    #        else:
-    #            bsO = 0
-    #        assert(bsO == self.other_basis_size)
-    #
-    #        if hamGens is not None:
-    #            hamGens_1norms = _np.array([_mt.safe_onenorm(mx) for mx in hamGens], 'd')
-    #        else:
-    #            hamGens_1norms = None
-    #
-    #        if otherGens is not None:
-    #            if self.parameterization.nonham_mode == "diagonal":
-    #                otherGens_1norms = _np.array([_mt.safe_onenorm(mx) for mx in otherGens], 'd')
-    #            else:
-    #                otherGens_1norms = _np.array([_mt.safe_onenorm(mx)
-    #                                              for oGenRow in otherGens for mx in oGenRow], 'd')
-    #        else:
-    #            otherGens_1norms = None
-    #
-    #        self._generators_cache[cache_key] = (hamGens, otherGens, hamGens_1norms, otherGens_1norms)
-    #
-    #    cached_hamGens, cached_otherGens, cached_h1norms, cached_o1norms = self._generators_cache[cache_key]
-    #    return (_copy.deepcopy(cached_hamGens), _copy.deepcopy(cached_otherGens),
-    #            cached_h1norms.copy() if (cached_h1norms is not None) else None,
-    #            cached_o1norms.copy() if (cached_o1norms is not None) else None)
+        # Done with __init__(...)
 
     def _init_terms(self, coefficient_blocks, max_polynomial_vars):
 
@@ -1341,7 +1208,7 @@ def transform_inplace(self, s):
 
             #conjugate Lindbladian exponent by U:
             err_gen_mx = self.to_sparse() if self._rep_type == 'sparse superop' else self.to_dense()
-            err_gen_mx = _mt.safe_dot(Uinv, _mt.safe_dot(err_gen_mx, U))
+            err_gen_mx = Uinv  @ (err_gen_mx @ U)
             trunc = 1e-6 if isinstance(s, _gaugegroup.UnitaryGaugeGroupElement) else False
             self._set_params_from_matrix(err_gen_mx, truncate=trunc)
             self.dirty = True