quick fixes

toqito/channel_metrics/completely_bounded_trace_norm.py

@@ -44,7 +44,7 @@ def completely_bounded_trace_norm(phi: np.ndarray) -> float:
     if is_quantum_channel(phi):
         return 1
 
-    elif is_completely_positive(phi):
+    if is_completely_positive(phi):
         v = apply_channel(np.eye(dim_ly), dual_channel(phi))
         return trace_norm(v)
 

toqito/channels/partial_trace.py

@@ -12,7 +12,7 @@
 
 def partial_trace(
     input_mat: np.ndarray | Variable,
-    sys: int | list[int] = [1],
+    sys: int | list[int] = None,
     dim: int | list[int] = None,
 ) -> np.ndarray | Expression:
     r"""
@@ -130,6 +130,9 @@ def partial_trace(
                 equal.
     :return: The partial trace of matrix :code:`input_mat`.
     """
+    if not isinstance(sys, int):
+        if sys is None:
+            sys = [1]
     # If the input matrix is a CVX variable for an SDP, we convert it to a numpy array,
     # perform the partial trace, and convert it back to a CVX variable.
     if isinstance(input_mat, Variable):
@@ -145,10 +148,10 @@ def partial_trace(
     if isinstance(dim, list):
         dim = np.array(dim)
 
-    num_sys = len(dim)
+
 
     # Allow the user to enter a single number for dim.
-    if num_sys == 1:
+    if (num_sys := len(dim)) == 1:
         dim = np.array([dim[0], len(input_mat) / dim[0]])
         if np.abs(dim[1] - np.round(dim[1])) >= 2 * len(input_mat) * np.finfo(float).eps:
             raise ValueError(

toqito/channels/partial_transpose.py

@@ -12,7 +12,7 @@
 
 def partial_transpose(
     rho: np.ndarray | Variable,
-    sys: list[int] | np.ndarray | int = [1],
+    sys: list[int] | np.ndarray | int = 1,
     dim: list[int] | np.ndarray = None,
 ) -> np.ndarray | Expression:
     r"""Compute the partial transpose of a matrix [WikPtrans]_.
@@ -135,9 +135,9 @@ def partial_transpose(
     if isinstance(sys, int):
         sys = np.array([sys])
 
-    num_sys = max(dim.shape)
+
     # Allow the user to enter a single number for dim.
-    if num_sys == 1:
+    if (num_sys := max(dim.shape)) == 1:
         dim = np.array([dim, list(rho.shape)[0] / dim])
         if np.abs(dim[1] - np.round(dim[1]))[0] >= 2 * list(rho.shape)[0] * np.finfo(float).eps:
             raise ValueError(

toqito/helper/npa_hierarchy.py

@@ -124,7 +124,7 @@ def _get_nonlocal_game_params(
     return a_out, a_in, b_out, b_in
 
 
-def npa_constraints(
+def npa_constraints(# pylint: too-many-locals
     assemblage: dict[tuple[int, int], cvxpy.Variable], k: int | str = 1, referee_dim: int = 1
 ) -> list[cvxpy.constraints.constraint.Constraint]:
     r"""

toqito/random/random_povm.py

@@ -49,10 +49,7 @@ def random_povm(dim: int, num_inputs: int, num_outputs: int) -> np.ndarray:
     povms = []
     gram_vectors = np.random.normal(size=(num_inputs, num_outputs, dim, dim))
     for input_block in gram_vectors:
-        normalizer = sum(
-            [np.array(output_block).T.conj() @ output_block for output_block in input_block]
-        )
-
+        normalizer = sum(np.array(output_block).T.conj() @ output_block for output_block in input_block)
         u_mat, d_mat, _ = np.linalg.svd(normalizer)
 
         output_povms = []

toqito/state_metrics/fidelity_of_separability.py

@@ -12,12 +12,12 @@
 from toqito.state_props import is_pure, is_separable
 
 
-def fidelity_of_separability(# pylint: disable = missing-type-doc
+def fidelity_of_separability(
     input_state_rho: np.ndarray,
     input_state_rho_dims: list[int],
     k: int = 1,
-    verbosity_option=2,
-    solver_option="cvxopt",
+    verbosity_option: int=2,
+    solver_option: str ="cvxopt",
 ) -> float:
     r"""
     Define the first benchmark introduced in Appendix H of [Phil23]_.

toqito/state_opt/state_exclusion.py

@@ -6,7 +6,7 @@
 
 
 def state_exclusion(
-    vectors: list[np.ndarray], probs: list[float] = None, solver: str = "cvxopt", primal_dual="dual"
+    vectors: list[np.ndarray], probs: list[float] = None, solver: str = "cvxopt", primal_dual: str ="dual"
 ) -> tuple[float, list[picos.HermitianVariable]]:
     r"""
     Compute probability of single state exclusion.
@@ -86,16 +86,19 @@ def state_exclusion(
         Physical Review A 89.2 (2014): 022336.
         arXiv:1306.4683
 
-    :param states: A list of states provided as vectors.
+    :param vectors: A list of states provided as vectors.
     :param probs: Respective list of probabilities each state is selected. If no
                   probabilities are provided, a uniform probability distribution is assumed.
+    :param solver: Optimization option for `picos` solver. Default option is 
+        `solver_option="cvxopt"`.
+    :param primal_dual: Option for the optimization problem
     :return: The optimal probability with which Bob can guess the state he was
              not given from `states` along with the optimal set of measurements.
     """
     if primal_dual == "primal":
         return _min_error_primal(vectors, probs, solver)
-    else:
-        return _min_error_dual(vectors, probs, solver)
+
+    return _min_error_dual(vectors, probs, solver)
 
 
 def _min_error_primal(vectors: list[np.ndarray], probs: list[float] = None, solver: str = "cvxopt"):

toqito/state_props/is_product.py

@@ -88,11 +88,10 @@ def _is_product(rho: np.ndarray, dim: int | list[int] = None) -> list[int, bool]
     # If there are only two subsystems, just use the Schmidt decomposition.
     if num_sys == 2:
         singular_vals, u_mat, vt_mat = schmidt_decomposition(rho, dim, 2)
-        ipv = singular_vals[1] <= np.prod(dim) * np.spacing(singular_vals[0])
 
         # Provide this even if not requested, since it is needed if this
         # function was called as part of its recursive algorithm (see below)
-        if ipv:
+        if (ipv := singular_vals[1] <= np.prod(dim) * np.spacing(singular_vals[0])):
             u_mat = u_mat * np.sqrt(singular_vals[0])
             vt_mat = vt_mat * np.sqrt(singular_vals[0])
             dec = [u_mat[:, 0], vt_mat[:, 0]]

toqito/state_props/is_separable.py

@@ -7,7 +7,7 @@
 
 from toqito.channels import realignment
 from toqito.matrix_props import is_positive_semidefinite, trace_norm
-from toqito.perms import swap
+
 from toqito.state_props import in_separable_ball, is_ppt
 from toqito.state_props.has_symmetric_extension import has_symmetric_extension
 
@@ -161,10 +161,11 @@ def is_separable(
 
     # For the rest of the block-matrix tests, we need the 2-dimensional subsystem to be the
     # first subsystem, so swap accordingly.
-    if dim[0] > 2:
-        Xt = swap(state, [1, 2], dim)
-    else:
-        Xt = state
+    #if dim[0] > 2:
+    #    Xt = swap(state, [1, 2], dim)
+    #else:
+    #    Xt = state
+    # commented out because pylint flagged this as an unused variable
 
     # Check the proximity of X with the maximally mixed state.
     if in_separable_ball(state):
@@ -182,7 +183,6 @@ def is_separable(
         return True
 
     # The search for symmetric extensions.
-    for _ in range(2, level):
-        if has_symmetric_extension(state, level):
-            return True
+    if any(has_symmetric_extension(state, level) for _ in range(2, level)):
+        return True
     return False

toqito/states/basis.py

@@ -48,7 +48,7 @@ def basis(dim: int, pos: int) -> np.ndarray:
             "Invalid: The `pos` variable needs to be less than `dim` for ket function."
         )
 
-    ret = np.array(list(map(int, list(f"{0:0{dim}}"))))
+    ret = np.array(list(int(x) for x in list(f"{0:0{dim}}")))
     ret[pos] = 1
     ret = ret.conj().T.reshape(-1, 1)
     return ret