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Merge pull request #452 from sandialabs/remove-unused-function-defs
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Remove commented-out function definitions and delete files with no executable code
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@sserita
sserita authored Jan 13, 2025
2 parents 504b83b + bf50f6e commit 6905edf
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Showing 91 changed files with 832 additions and 9,037 deletions.
1 change: 0 additions & 1 deletion .github/CODEOWNERS
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Expand Up @@ -37,7 +37,6 @@ pygsti/algorithms/compilers.py @sandialabs/pygsti-rb @sandialabs/pygsti-gatekeep
pygsti/algorithms/mirroring.py @sandialabs/pygsti-rb @sandialabs/pygsti-gatekeepers
pygsti/algorithms/randomcircuit.py @sandialabs/pygsti-rb @sandialabs/pygsti-gatekeepers
pygsti/algorithms/rbfit.py @sandialabs/pygsti-rb @sandialabs/pygsti-gatekeepers
pygsti/extras/rb.py @sandialabs/pygsti-rb @sandialabs/pygsti-gatekeepers # Should this just be deprecated and removed?
pygsti/protocols/rb.py @sandialabs/pygsti-rb @sandialabs/pygsti-gatekeepers
pygsti/tools/rbtheory.py @sandialabs/pygsti-rb @sandialabs/pygsti-gatekeepers
pygsti/tools/rbtools.py @sandialabs/pygsti-rb @sandialabs/pygsti-gatekeepers
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2 changes: 0 additions & 2 deletions pygsti/algorithms/core.py
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Expand Up @@ -1149,8 +1149,6 @@ def find_closest_unitary_opmx(operation_mx):
# d = _np.sqrt(operation_mx.shape[0])
# I = _np.identity(d)

#def getu_1q(basisVec): # 1 qubit version
# return _spl.expm( 1j * (basisVec[0]*_tools.sigmax + basisVec[1]*_tools.sigmay + basisVec[2]*_tools.sigmaz) )
def _get_gate_mx_1q(basis_vec): # 1 qubit version
return _tools.single_qubit_gate(basis_vec[0],
basis_vec[1],
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7 changes: 0 additions & 7 deletions pygsti/algorithms/fiducialselection.py
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Expand Up @@ -409,13 +409,6 @@ def final_result_test(final_fids, verb_printer):
return prepFidList, measFidList


#def bool_list_to_ind_list(boolList):
# output = _np.array([])
# for i, boolVal in boolList:
# if boolVal == 1:
# output = _np.append(i)
# return output

def xor(*args):
"""
Implements logical xor function for arbitrary number of inputs.
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147 changes: 74 additions & 73 deletions pygsti/algorithms/germselection.py
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Expand Up @@ -3295,80 +3295,81 @@ def symmetric_low_rank_spectrum_update(update, orig_e, U, proj_U, force_rank_inc
#return the new eigenvalues
return new_evals, True

#Note: This function won't work for our purposes because of the assumptions
#about the rank of the update on the nullspace of the matrix we're updating,
#but keeping this here commented for future reference.
#Function for doing fast calculation of the updated inverse trace:
#def riedel_style_inverse_trace(update, orig_e, U, proj_U, force_rank_increase=True):
# """
# input:
#
# update : ndarray
# symmetric low-rank update to perform.
# This is the first half the symmetric rank decomposition s.t.
# update@update.T= the full update matrix.
#
# orig_e : ndarray
# Spectrum of the original matrix. This is a 1-D array.
#
# proj_U : ndarray
# Projector onto the complement of the column space of the
# original matrix's eigenvectors.
#
# output:
#
# trace : float
# Value of the trace of the updated psuedoinverse matrix.
#
# updated_rank : int
# total rank of the updated matrix.
#
# rank_increase_flag : bool
# a flag that is returned to indicate is a candidate germ failed to amplify additional parameters.
# This indicates things short circuited and so the scoring function should skip this germ.
# """
#
# #First we need to for the matrix P, whose column space
# #forms an orthonormal basis for the component of update
# #that is in the complement of U.
#
# proj_update= proj_U@update
#
# #Next take the RRQR decomposition of this matrix:
# q_update, r_update, _ = _sla.qr(proj_update, mode='economic', pivoting=True)
#
# #Construct P by taking the columns of q_update corresponding to non-zero values of r_A on the diagonal.
# nonzero_indices_update= _np.nonzero(_np.diag(r_update)>1e-10) #HARDCODED (threshold is hardcoded)
#
# #if the rank doesn't increase then we can't use the Riedel approach.
# #Abort early and return a flag to indicate the rank did not increase.
# if len(nonzero_indices_update[0])==0 and force_rank_increase:
# return None, None, False
#
# P= q_update[: , nonzero_indices_update[0]]
#
# updated_rank= len(orig_e)+ len(nonzero_indices_update[0])
#
# #Now form the matrix R_update which is given by P.T @ proj_update.
# R_update= P.T@proj_update
#
# #R_update gets concatenated with U.T@update to form
# #a block column matrixblock_column= np.concatenate([U.T@update, R_update], axis=0)
#
# Uta= U.T@update
#
# try:
# RRRDinv= R_update@_np.linalg.inv(R_update.T@R_update)
# except _np.linalg.LinAlgError as err:
# print('Numpy thinks this matrix is singular, condition number is: ', _np.linalg.cond(R_update.T@R_update))
# print((R_update.T@R_update).shape)
# raise err
# pinv_orig_e_mat= _np.diag(1/orig_e)
#
# trace= _np.sum(1/orig_e) + _np.trace( RRRDinv@(_np.eye(Uta.shape[1]) + Uta.T@pinv_orig_e_mat@Uta)@RRRDinv.T )
#
# return trace, updated_rank, True
# Note: Th function below won't work for our purposes because of the assumptions
# about the rank of the update on the nullspace of the matrix we're updating,
# but keeping this here commented for future reference.
'''
def riedel_style_inverse_trace(update, orig_e, U, proj_U, force_rank_increase=True):
"""
input:
update : ndarray
symmetric low-rank update to perform.
This is the first half the symmetric rank decomposition s.t.
update@update.T= the full update matrix.
orig_e : ndarray
Spectrum of the original matrix. This is a 1-D array.
proj_U : ndarray
Projector onto the complement of the column space of the
original matrix's eigenvectors.
output:
trace : float
Value of the trace of the updated psuedoinverse matrix.
updated_rank : int
total rank of the updated matrix.
rank_increase_flag : bool
a flag that is returned to indicate is a candidate germ failed to amplify additional parameters.
This indicates things short circuited and so the scoring function should skip this germ.
"""
#First we need to for the matrix P, whose column space
#forms an orthonormal basis for the component of update
#that is in the complement of U.
proj_update= proj_U@update
#Next take the RRQR decomposition of this matrix:
q_update, r_update, _ = _sla.qr(proj_update, mode='economic', pivoting=True)
#Construct P by taking the columns of q_update corresponding to non-zero values of r_A on the diagonal.
nonzero_indices_update= _np.nonzero(_np.diag(r_update)>1e-10) #HARDCODED (threshold is hardcoded)
#if the rank doesn't increase then we can't use the Riedel approach.
#Abort early and return a flag to indicate the rank did not increase.
if len(nonzero_indices_update[0])==0 and force_rank_increase:
return None, None, False
P= q_update[: , nonzero_indices_update[0]]
updated_rank= len(orig_e)+ len(nonzero_indices_update[0])
#Now form the matrix R_update which is given by P.T @ proj_update.
R_update= P.T@proj_update
#R_update gets concatenated with U.T@update to form
#a block column matrixblock_column= np.concatenate([U.T@update, R_update], axis=0)
Uta= U.T@update
try:
RRRDinv= R_update@_np.linalg.inv(R_update.T@R_update)
except _np.linalg.LinAlgError as err:
print('Numpy thinks this matrix is singular, condition number is: ', _np.linalg.cond(R_update.T@R_update))
print((R_update.T@R_update).shape)
raise err
pinv_orig_e_mat= _np.diag(1/orig_e)
trace= _np.sum(1/orig_e) + _np.trace( RRRDinv@(_np.eye(Uta.shape[1]) + Uta.T@pinv_orig_e_mat@Uta)@RRRDinv.T )
return trace, updated_rank, True
'''

def minamide_style_inverse_trace(update, orig_e, U, proj_U, force_rank_increase=False):
"""
This function performs a low-rank update to the components of
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