Masking functionality for PauliwordOp.__getitem__

symmer/projection/contextual_subspace.py

@@ -29,7 +29,7 @@ def manual_stabilizers(self, S: Union[List[str], StabilizerOp]) -> None:
  """
  """
  if isinstance(S, list):
- S = StabilizerOp.from_list(S_list)
+ S = StabilizerOp.from_list(S)
  self.n_qubits_in_subspace = self.operator.n_qubits - S.n_terms
  self.stabilizers = S
 

symmer/projection/utils.py

@@ -72,7 +72,7 @@ def build_basis_weighting_operator(self):
  np.abs(self.weighting_operator.coeff_vec)
  ).cleanup()
  #X_op = self.weighting_operator
- self.basis_weighting = X_op.sort(key='magnitude')
+ self.basis_weighting = X_op.sort(by='magnitude')
  self.qubit_positions = np.arange(self.weighting_operator.n_qubits)
  self.term_region = [0,self.basis_weighting.n_terms]
 

symmer/symplectic/base.py

@@ -293,27 +293,27 @@ def copy(self) -> "PauliwordOp":
  return deepcopy(self)
 
  def sort(self, 
- by: str = 'decreasing',
- key:str = 'magnitude'
+ by:  str = 'magnitude',
+ key: str = 'decreasing'
  ) -> "PauliwordOp":
  """
- Sort the terms by some key, either magnitude, weight X, Y or Z
+ Sort the terms either by magnitude, weight X, Y or Z
  """
- if key=='magnitude':
+ if by=='magnitude':
  sort_order = np.argsort(-abs(self.coeff_vec))
- elif key=='weight':
- sort_order = np.argsort(-np.sum(self.symp_matrix, axis=1))
- elif key=='Z':
- sort_order = np.argsort(np.sum((self.n_qubits+1)*self.X_block + self.Z_block, axis=1))
- elif key=='X':
- sort_order = np.argsort(np.sum(self.X_block + (self.n_qubits+1)*self.Z_block, axis=1))
- elif key=='Y':
- sort_order = np.argsort(np.sum(abs(self.X_block - self.Z_block), axis=1))
+ elif by=='weight':
+ sort_order = np.argsort(-np.sum(self.symp_matrix.astype(int), axis=1))
+ elif by=='Z':
+ sort_order = np.argsort(np.sum((self.n_qubits+1)*self.X_block.astype(int) + self.Z_block.astype(int), axis=1))
+ elif by=='X':
+ sort_order = np.argsort(np.sum(self.X_block.astype(int) + (self.n_qubits+1)*self.Z_block.astype(int), axis=1))
+ elif by=='Y':
+ sort_order = np.argsort(np.sum(abs(self.X_block.astype(int) - self.Z_block.astype(int)), axis=1))
  else:
- raise ValueError('Only permitted sort key values are magnitude, weight, X, Y or Z')
- if by=='increasing':
+ raise ValueError('Only permitted sort by values are magnitude, weight, X, Y or Z')
+ if key=='increasing':
  sort_order = sort_order[::-1]
- elif by!='decreasing':
+ elif key!='decreasing':
  raise ValueError('Only permitted sort by values are increasing or decreasing')
  return PauliwordOp(self.symp_matrix[sort_order], self.coeff_vec[sort_order])
 
@@ -572,6 +572,10 @@ def __getitem__(self,
  if stop is None:
  stop=self.n_terms
  mask = np.arange(start, stop, key.step)
+ elif isinstance(key, (list, np.ndarray)):
+ mask = np.asarray(key)
+ else:
+ raise ValueError('Unrecognised input, must be an integer, slice, list or np.array')
 
  symp_items = self.symp_matrix[mask]
  coeff_items = self.coeff_vec[mask]
@@ -809,7 +813,7 @@ def clique_cover(self,
  if colouring_interchange is not False:
  warnings.warn(f'{strategy} is not a graph colouring method, so colouring_interchange flag is ignored')
 
- sorted_op_list = list(self.sort(by='decreasing', key='magnitude'))
+ sorted_op_list = list(self.sort(by='magnitude', key='decreasing'))
 
  check_dic = {
  'C': lambda x, y: np.all(x.commutes_termwise(y)),

symmer/symplectic/noncontextual_op.py

@@ -68,7 +68,7 @@ def _dfs_noncontextual_op(cls, H: PauliwordOp, runtime=10, strategy='magnitude')
  cutoff time ensures if the number of possibilities is large the function will STOP and not take too long
 
  """
- operator = H.sort(key='magnitude')
+ operator = H.sort(by='magnitude')
  noncontextual_ops = []
 
  n=0
@@ -102,7 +102,7 @@ def _diag_first_noncontextual_op(cls, H: PauliwordOp):
  """
  noncontextual_operator = cls._diag_noncontextual_op(H)
  # order the remaining terms by coefficient magnitude
- off_diag_terms = (H - noncontextual_operator).sort(key='magnitude')
+ off_diag_terms = (H - noncontextual_operator).sort(by='magnitude')
  # append terms that do not make the noncontextual_operator contextual!
  for term in off_diag_terms:
  if (noncontextual_operator+term).is_noncontextual:
@@ -117,7 +117,7 @@ def _single_sweep_noncontextual_operator(cls, H, strategy='magnitude'):
  noncontextual_operator = PauliwordOp.empty(H.n_qubits)
 
  if strategy=='magnitude':
- operator = H.sort(key='magnitude')
+ operator = H.sort(by='magnitude')
  elif strategy=='random':
  order = np.arange(H.n_terms)
  np.random.shuffle(order)