/
fogitools.py
1104 lines (955 loc) · 68 KB
/
fogitools.py
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"""
Utility functions for computing and working with first-order-gauge-invariant (FOGI) quantities.
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# in this software.
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory.
#***************************************************************************************************
import numpy as _np
import scipy.sparse as _sps
import scipy.sparse.linalg as _spsl
from . import matrixtools as _mt
from . import optools as _ot
def first_order_gauge_action_matrix(clifford_superop_mx, target_sslbls, model_state_space,
elemgen_gauge_basis, elemgen_row_basis):
"""
Returns a matrix for computing the *offset* of a given gate's error generator due to a local gauge action.
Note: clifford_superop must be in the *std* basis!
TODO: docstring
"""
#Utilize EmbeddedOp to perform superop embedding
from pygsti.modelmembers.operations import EmbeddedOp as _EmbeddedOp, StaticArbitraryOp as _StaticArbitraryOp
from pygsti.baseobjs.errorgenbasis import ExplicitElementaryErrorgenBasis as _ExplicitElementaryErrorgenBasis
def _embed(mx, target_labels, state_space): # BOTTLENECK
if mx.shape[0] == state_space.dim and target_labels == state_space.sole_tensor_product_block_labels:
return mx # no embedding needed
else:
dummy_op = _EmbeddedOp(state_space, target_labels,
_StaticArbitraryOp(_np.identity(mx.shape[0], 'd'), None, 'densitymx_slow'))
embeddedOp = _sps.identity(state_space.dim, mx.dtype, format='lil')
#fill in embedded_op contributions (always overwrites the diagonal
# of finalOp where appropriate, so OK it starts as identity)
for i, j, gi, gj in dummy_op._iter_matrix_elements('HilbertSchmidt'):
embeddedOp[i, j] = mx[gi, gj]
#return embeddedOp.tocsr() # Note: if sparse, assure that this is in CSR or CSC for fast products
return embeddedOp.toarray()
action_mx = _sps.lil_matrix((len(elemgen_row_basis), len(elemgen_gauge_basis)), dtype=clifford_superop_mx.dtype)
nonzero_rows = set()
nonzero_row_labels = {}
TOL = 1e-12 # ~ machine precision
#print("DB fogi action mx: outer iter, initial action mx shape = %s" % (str(action_mx.shape))) # DEBUG REMOVE
#print("DB superop = \n", clifford_superop_mx) # DEBUG REMOVE
#import bpdb; bpdb.set_trace() # REMOVE
#db_seen_sslbls = set() # DEBUG!!! REMOVE
for j, (gen_sslbls, gen) in enumerate(elemgen_gauge_basis.elemgen_supports_and_matrices): # BOTTLENECK eval attribute
action_sslbls = tuple(sorted(set(gen_sslbls).union(target_sslbls))) # (union) - joint support of ops
action_space = model_state_space.create_subspace(action_sslbls)
gen_expanded = _embed(gen, gen_sslbls, action_space) # expand gen to shared action_space
U_expanded = _embed(clifford_superop_mx, target_sslbls, action_space) # expand to shared action_space
if _sps.issparse(gen_expanded):
conjugated_gen = U_expanded.dot(gen_expanded.dot(U_expanded.transpose().conjugate())) # sparse matrices
else:
conjugated_gen = _np.dot(U_expanded, _np.dot(gen_expanded, _np.conjugate(U_expanded.T)))
gauge_action_deriv = gen_expanded - conjugated_gen # (on action_space)
action_row_basis = elemgen_row_basis.create_subbasis(action_sslbls) # spans all *possible* error generators
# - a full basis for gauge_action_deriv
#global_row_space.add_labels(row_space.labels) # labels would need to contain sslbls too
action_row_labels = action_row_basis.labels
global_row_indices = elemgen_row_basis.label_indices(action_row_labels, ok_if_missing=True)
#DEBUG REMOVE
#db_num_skipped = db_num_nonzero = 0
#db_row_lbl = elemgen_gauge_basis.labels[j]
#print("U_expanded embeds ", target_sslbls, " onto ", action_sslbls) # DEBUG
#print("gen_expanded embeds ", db_row_lbl, gen_sslbls, " onto ", action_sslbls) # DEBUG
#import bpdb; bpdb.set_trace()
#if gen_sslbls not in db_seen_sslbls:
# db_num_skipped = db_num_nonzero = 0
# print("DB fogi action mx: inner iter for gen_sslbls=%s; action_sslbls=%s, action_row_basis size = %d"
# % (str(gen_sslbls), str(action_sslbls), len(global_row_indices)))
# Note: can avoid this projection and conjugation math above if we know gen is Pauli action and U is clifford
for i, row_label, (gen2_sslbls, gen2) in zip(global_row_indices, action_row_labels,
action_row_basis.elemgen_supports_and_dual_matrices):
#if not is_subset(gen2_sslbls, space):
if not set(gen2_sslbls).issubset(action_sslbls):
#print(" -> ", row_label, ' skipped b/c gen ssbls not fully within action space') # REMOVE
#db_num_skipped += 1 # REMOVE
continue # no overlap/component when gen2 is nontrivial (and assumed orthogonal to identity)
# on a factor space where gauge_action_deriv is zero
#TODO: add more shortcuts here, e.g., if errorgens are different H/S types result is zero?
# When we embed gen2 (a *dual* generator) into action_space, we really want the *dual* of
# the identity to act on the complement of gen2's space, not the identity itself, to create
# an embedded dual generator. Since Tr(I I) = Tr(I) = 4^(number of non-embedded qubits), we
# simply scale by 1/4^(num non-embedded) below.
scale = 4**len(gen2_sslbls) / action_space.dim # 4^(number of non-embedded qubits)
gen2_expanded = _embed(gen2, gen2_sslbls, action_space) # embed gen2 into action_space
gen2_expanded *= scale # so that gen2_expanded is an embedded *dual* generator
if _sps.issparse(gen2_expanded):
flat_gen2_expanded = gen2_expanded.reshape((1, _np.prod(gen2_expanded.shape)))
flat_gauge_action_deriv = gauge_action_deriv.reshape((_np.prod(gauge_action_deriv.shape), 1))
val = flat_gen2_expanded.dot(flat_gauge_action_deriv)[0, 0] # Note: gen2 is a *dual* generator
else:
val = _np.vdot(gen2_expanded.flat, gauge_action_deriv.flat)
assert(abs(val.imag) < TOL) # all values should be real, I think...
if abs(val) > TOL:
#print(" -> ", db_row_lbl, ", ", row_label, ' val = ', val) # REMOVE
#db_num_nonzero += 1 # REMOVE
if i not in nonzero_rows:
nonzero_rows.add(i)
nonzero_row_labels[i] = row_label
action_mx[i, j] = val
#DEBUG REMOVE
#if gen_sslbls not in db_seen_sslbls:
#print(" -- skipped %d, processed %d -- %d of which were nonzero" % (db_num_skipped, len(global_row_indices) - db_num_skipped, db_num_nonzero))
#db_seen_sslbls.add(gen_sslbls)
#TODO HERE: check that decomposition into components adds to entire gauge_action_deriv
# (checks "completeness" of row basis)
#return action_mx
#Remove all all-zero rows and cull these elements out of the row_basis. Actually,
# just construct a new matrix and basis
#print("DB fogi action mx: beginning matrix reduction...") # REMOVE
nonzero_row_indices = list(sorted(nonzero_rows))
labels = [nonzero_row_labels[i] for i in nonzero_row_indices]
data = []; col_indices = []; rowptr = [0] # build up a CSR matrix manually from nonzero rows
for ii, i in enumerate(nonzero_row_indices):
col_indices.extend(action_mx.rows[i])
data.extend(action_mx.data[i])
rowptr.append(len(data))
culled_action_mx = _sps.csr_matrix((data, col_indices, rowptr),
shape=(len(nonzero_rows), len(elemgen_gauge_basis)), dtype=action_mx.dtype)
updated_row_basis = _ExplicitElementaryErrorgenBasis(elemgen_row_basis.state_space, labels)
#print("DB fogi action mx: culled action matrix to shape", culled_action_mx.shape) # REMOVE
return culled_action_mx, updated_row_basis
def first_order_gauge_action_matrix_for_prep(prep_superket_vec, target_sslbls, model_state_space,
elemgen_gauge_basis, elemgen_row_basis):
"""
Returns a matrix for computing the *offset* of a given gate's error generator due to a local gauge action.
Note: clifford_superop must be in the *std* basis!
TODO: docstring
"""
#Utilize EmbeddedOp to perform superop embedding
from pygsti.modelmembers.operations import EmbeddedOp as _EmbeddedOp, StaticArbitraryOp as _StaticArbitraryOp
from pygsti.baseobjs.errorgenbasis import ExplicitElementaryErrorgenBasis as _ExplicitElementaryErrorgenBasis
def _embed(mx, target_labels, state_space): # SAME as in fn above
if mx.shape[0] == state_space.dim and target_labels == state_space.sole_tensor_product_block_labels:
return mx # no embedding needed
else:
dummy_op = _EmbeddedOp(state_space, target_labels,
_StaticArbitraryOp(_np.identity(mx.shape[0], 'd'), None, 'densitymx_slow'))
embeddedOp = _sps.identity(state_space.dim, mx.dtype, format='lil')
scale = _np.sqrt(4**len(target_labels) / state_space.dim) # is this correct??
#fill in embedded_op contributions (always overwrites the diagonal
# of finalOp where appropriate, so OK it starts as identity)
for i, j, gi, gj in dummy_op._iter_matrix_elements('HilbertSchmidt'):
embeddedOp[i, j] = mx[gi, gj] * scale
#return embeddedOp.tocsr() # Note: if sparse, assure that this is in CSR or CSC for fast products
return embeddedOp.toarray()
element_action_mx = _sps.lil_matrix((prep_superket_vec.shape[0], len(elemgen_gauge_basis)),
dtype=prep_superket_vec.dtype)
for j, (gen_sslbls, gen) in enumerate(elemgen_gauge_basis.elemgen_supports_and_matrices):
action_sslbls = tuple(sorted(set(gen_sslbls).union(target_sslbls))) # (union) - joint support of ops
action_space = model_state_space.create_subspace(action_sslbls)
#Note: action_space is always (?) going to be the full model_state_space, since target_sslbls for a prep
# should always be all the sslbls of the model (I think...)
gen_expanded = _embed(gen, gen_sslbls, action_space) # expand gen to shared action_space
if _sps.issparse(gen_expanded):
gauge_action_deriv = gen_expanded.dot(prep_superket_vec) # sparse matrices
else:
gauge_action_deriv = _np.dot(gen_expanded, prep_superket_vec)
element_action_mx[:, j] = gauge_action_deriv[:, None]
#To identify set of vectors {v_i} such that {element_action_mx * v_i} span the range of element_action_mx,
# we find the SVD of element_action_mx and use the columns of V:
TOL = 1e-7
U, s, Vh = _np.linalg.svd(element_action_mx.toarray(), full_matrices=False) # DENSE - use sparse SVD here?
n = _np.count_nonzero(s > TOL)
relevant_basis = Vh[0:n, :].T.conjugate()
for j in range(relevant_basis.shape[1]): # normalize columns so largest element is +1.0
i_max = _np.argmax(_np.abs(relevant_basis[:, j]))
if abs(relevant_basis[i_max, j]) > 1e-6:
relevant_basis[:, j] /= relevant_basis[i_max, j]
relevant_basis = _mt.normalize_columns(relevant_basis)
# "gauge action" matrix is just the identity on the *relevant* space of gauge transformations:
action_mx_pre = _np.dot(relevant_basis, relevant_basis.T.conjugate()) # row basis == elemgen_gauge_basis
action_mx = _sps.lil_matrix((len(elemgen_row_basis), len(elemgen_gauge_basis)),
dtype=prep_superket_vec.dtype)
nonzero_rows = set()
nonzero_row_labels = {}
#Convert row-space to be over elemgen_row_basis instead of a elemgen_gauge_basis
for i, glbl in enumerate(elemgen_gauge_basis.labels):
new_i = elemgen_row_basis.label_index(glbl)
if _np.linalg.norm(action_mx_pre[i, :]) > 1e-8:
action_mx[new_i, :] = action_mx_pre[i, :]
nonzero_rows.add(new_i)
nonzero_row_labels[new_i] = glbl
#Remove all all-zero rows and cull these elements out of the row_basis. Actually,
# just construct a new matrix and basis
nonzero_row_indices = list(sorted(nonzero_rows))
labels = [nonzero_row_labels[i] for i in nonzero_row_indices]
data = []; col_indices = []; rowptr = [0] # build up a CSR matrix manually from nonzero rows
for i in nonzero_row_indices:
col_indices.extend(action_mx.rows[i]) # .rows[i] is list of column indices of i-th row.
data.extend(action_mx.data[i]) # .data[i] is i-th row for a lil (list-of-lists) matrix
rowptr.append(len(data))
culled_action_mx = _sps.csr_matrix((data, col_indices, rowptr),
shape=(len(nonzero_rows), len(elemgen_gauge_basis)), dtype=action_mx.dtype)
updated_row_basis = _ExplicitElementaryErrorgenBasis(elemgen_row_basis.state_space, labels)
return culled_action_mx, updated_row_basis
def first_order_gauge_action_matrix_for_povm(povm_superbra_vecs, target_sslbls, model_state_space,
elemgen_gauge_basis, elemgen_row_basis):
"""
Returns a matrix for computing the *offset* of a given gate's error generator due to a local gauge action.
Note: clifford_superop must be in the *std* basis!
TODO: docstring
"""
#Utilize EmbeddedOp to perform superop embedding
from pygsti.modelmembers.operations import EmbeddedOp as _EmbeddedOp, StaticArbitraryOp as _StaticArbitraryOp
from pygsti.baseobjs.errorgenbasis import ExplicitElementaryErrorgenBasis as _ExplicitElementaryErrorgenBasis
def _embed(mx, target_labels, state_space): # SAME as in fn above
if mx.shape[0] == state_space.dim and target_labels == state_space.sole_tensor_product_block_labels:
return mx # no embedding needed
else:
dummy_op = _EmbeddedOp(state_space, target_labels,
_StaticArbitraryOp(_np.identity(mx.shape[0], 'd'), None, 'densitymx_slow'))
embeddedOp = _sps.identity(state_space.dim, mx.dtype, format='lil')
scale = _np.sqrt(4**len(target_labels) / state_space.dim) # is this correct??
#fill in embedded_op contributions (always overwrites the diagonal
# of finalOp where appropriate, so OK it starts as identity)
for i, j, gi, gj in dummy_op._iter_matrix_elements('HilbertSchmidt'):
embeddedOp[i, j] = mx[gi, gj] * scale
#return embeddedOp.tocsr() # Note: if sparse, assure that this is in CSR or CSC for fast products
return embeddedOp.toarray()
element_action_mx = _sps.lil_matrix((sum([v.shape[0] for v in povm_superbra_vecs]), len(elemgen_gauge_basis)),
dtype=povm_superbra_vecs[0].dtype)
for j, (gen_sslbls, gen) in enumerate(elemgen_gauge_basis.elemgen_supports_and_matrices):
action_sslbls = tuple(sorted(set(gen_sslbls).union(target_sslbls))) # (union) - joint support of ops
action_space = model_state_space.create_subspace(action_sslbls)
#Note: action_space is always (?) going to be the full model_state_space, since target_sslbls for a prep
# should always be all the sslbls of the model (I think...)
#Currently, this applies same vector to *all* povm effects - i.e. treats as a ComposedPOVM
# Note: gauge acts on effects as: dot(v, -gen_expanded) = dot(-gen_expanded.T.conj, v)
gen_expanded = _embed(gen, gen_sslbls, action_space) # expand gen to shared action_space
if _sps.issparse(gen_expanded):
gauge_action_deriv = _sps.vstack([-gen_expanded.transpose().conjugate().dot(v) for v in povm_superbra_vecs])
else:
gauge_action_deriv = _np.concatenate([_np.dot(-gen_expanded.T.conjugate(), v) for v in povm_superbra_vecs])
element_action_mx[:, j] = gauge_action_deriv[:, None]
#FROM HERE DOWN ~same as for prep vector (concat effects treated like one big prep vector)
#To identify set of vectors {v_i} such that {element_action_mx * v_i} span the range of element_action_mx,
# we find the SVD of element_action_mx and use the columns of V:
TOL = 1e-7
U, s, Vh = _np.linalg.svd(element_action_mx.toarray(), full_matrices=False) # DENSE - use sparse SVD here?
n = _np.count_nonzero(s > TOL)
relevant_basis = Vh[0:n, :].T.conjugate()
for j in range(relevant_basis.shape[1]): # normalize columns so largest element is +1.0
i_max = _np.argmax(_np.abs(relevant_basis[:, j]))
if abs(relevant_basis[i_max, j]) > 1e-6:
relevant_basis[:, j] /= relevant_basis[i_max, j]
relevant_basis = _mt.normalize_columns(relevant_basis)
# "gauge action" matrix is just the -identity on the *relevant* space of gauge transformations:
action_mx_pre = -_np.dot(relevant_basis, relevant_basis.T.conjugate()) # row basis == elemgen_gauge_basis
action_mx = _sps.lil_matrix((len(elemgen_row_basis), len(elemgen_gauge_basis)),
dtype=povm_superbra_vecs[0].dtype)
nonzero_rows = set()
nonzero_row_labels = {}
#Convert row-space to be over elemgen_row_basis instead of a elemgen_gauge_basis
for i, glbl in enumerate(elemgen_gauge_basis.labels):
new_i = elemgen_row_basis.label_index(glbl)
if _np.linalg.norm(action_mx_pre[i, :]) > 1e-8:
action_mx[new_i, :] = action_mx_pre[i, :]
nonzero_rows.add(new_i)
nonzero_row_labels[new_i] = glbl
#Remove all all-zero rows and cull these elements out of the row_basis. Actually,
# just construct a new matrix and basis
nonzero_row_indices = list(sorted(nonzero_rows))
labels = [nonzero_row_labels[i] for i in nonzero_row_indices]
data = []; col_indices = []; rowptr = [0] # build up a CSR matrix manually from nonzero rows
for i in nonzero_row_indices:
col_indices.extend(action_mx.rows[i])
data.extend(action_mx.data[i])
rowptr.append(len(data))
culled_action_mx = _sps.csr_matrix((data, col_indices, rowptr),
shape=(len(nonzero_rows), len(elemgen_gauge_basis)), dtype=action_mx.dtype)
updated_row_basis = _ExplicitElementaryErrorgenBasis(elemgen_row_basis.state_space, labels)
return culled_action_mx, updated_row_basis
def _create_op_errgen_indices_dict(primitive_op_labels, errorgen_coefficient_labels):
op_errgen_indices = {}; off = 0 # tells us which indices of errgen-set space map to which ops
for op_label in primitive_op_labels:
num_coeffs = len(errorgen_coefficient_labels[op_label])
op_errgen_indices[op_label] = slice(off, off + num_coeffs)
off += num_coeffs
return op_errgen_indices
def construct_fogi_quantities(primitive_op_labels, gauge_action_matrices,
errorgen_coefficient_labels, op_errgen_indices, gauge_space,
op_label_abbrevs=None, dependent_fogi_action='drop', norm_order=None):
""" TODO: docstring """
assert(dependent_fogi_action in ('drop', 'mark'))
orthogonalize_relationals = True
#Get lists of the present (existing within the model) labels for each operation
if op_label_abbrevs is None: op_label_abbrevs = {}
if op_errgen_indices is None:
op_errgen_indices = _create_op_errgen_indices_dict(primitive_op_labels, errorgen_coefficient_labels)
num_elem_errgens = sum([len(labels) for labels in errorgen_coefficient_labels.values()])
#Step 1: construct FOGI quantities and reference frame for each op
ccomms = {}
fogi_dirs = _sps.csc_matrix((num_elem_errgens, 0), dtype=complex) # dual vectors ("directions") in eg-set space
fogi_meta = [] # elements correspond to matrix columns
dep_fogi_dirs = _sps.csc_matrix((num_elem_errgens, 0), dtype=complex) # dependent columns we still want to track
dep_fogi_meta = [] # elements correspond to matrix columns
def add_relational_fogi_dirs(dirs_to_add, gauge_vecs, gauge_dirs, initial_dirs, metadata,
existing_opset, new_op_label, new_opset, norm_orders):
""" Note: gauge_vecs and gauge_dirs are the same up to a normalization - maybe combine? """
vecs_to_add, nrms = _mt.normalize_columns(dirs_to_add, ord=norm_orders, return_norms=True) # f_hat_vec = f/nrm
vector_L2_norm2s = _mt.column_norms(vecs_to_add)**2 # L2 norm squared
vector_L2_norm2s[vector_L2_norm2s == 0.0] = 1.0 # avoid division of zero-column by zero
dirs_to_add = _mt.scale_columns(vecs_to_add, 1 / vector_L2_norm2s)
# above gives us *dir*-norm we want # DUAL NORM
# f_hat = f_hat_vec / L2^2 = f / (nrm * L2^2) = (1 / (nrm * L2^2)) * f
resulting_dirs = _sps.hstack((initial_dirs, dirs_to_add)) # errgen-space NORMALIZED
full_gauge_vecs = _np.dot(gauge_space.vectors, gauge_vecs) # in gauge_space's basis
gauge_names = elem_vec_names(full_gauge_vecs, gauge_space.elemgen_basis.labels)
gauge_names_abbrev = elem_vec_names(full_gauge_vecs, gauge_space.elemgen_basis.labels, include_type=False)
names = ["ga(%s)_%s - ga(%s)_%s" % (
iname, "|".join([op_label_abbrevs.get(l, str(l)) for l in existing_opset]),
iname, op_label_abbrevs.get(new_op_label, str(new_op_label))) for iname in gauge_names]
abbrev_names = ["ga(%s)" % iname for iname in gauge_names_abbrev]
for j, (name, name_abbrev, nrm, L2norm2) in enumerate(zip(names, abbrev_names, nrms, vector_L2_norm2s)):
metadata.append({'name': name, 'abbrev': name_abbrev, 'r': 1 / (nrm * L2norm2),
'gaugespace_dir': gauge_dirs[:, j], 'opset': new_opset})
# Note intersection_space is a subset of the *gauge-space*, and so its basis,
# placed under gaugespace_dirs keys, is for gauge-space, not errorgen-space.
#DEBUG REMOVE
#print("DB: name = ",name, " norm order = ",norm_orders[j])
#print("Norm order = ", norm_orders[j], ":", [el for el in vecs_to_add[:,j].toarray()[:,0] if not _np.isclose(el, 0)])
#print("Post scaling = ", [el for el in dirs_to_add[:,j].toarray()[:,0] if not _np.isclose(el, 0)], '\n')
return resulting_dirs
def resolve_norm_order(vecs_to_normalize, label_lists, given_norm_order):
"""Turn user-supplied norm-order into an array of norm orders based, sometimes, on the vecs being normalized """
if isinstance(given_norm_order, int):
norm_order_array = _np.ones(local_fogi_dirs.shape[1], dtype=_np.int64) * given_norm_order
elif given_norm_order == "auto": # automaticaly choose norm order based on fogi direction composition
lbl_lookup = {}; off = 0
for label_list in label_lists:
lbl_lookup.update({i + off: lbl for i, lbl in enumerate(label_list)})
off += len(label_list)
norm_order_array = []; TOL = 1e-8
for j in range(vecs_to_normalize.shape[1]):
lbl_types = set([lbl_lookup[i].errorgen_type for i, v in enumerate(vecs_to_normalize[:, j])
if abs(v) > TOL]) # a set of the errorgen types contributing to the jth vec
if lbl_types == set(['S']): norm_order_array.append(1)
else: norm_order_array.append(2)
norm_order_array = _np.array(norm_order_array, dtype=_np.int64)
else:
raise ValueError("Invalid norm_order: %s" % str(given_norm_order))
return norm_order_array
for op_label in primitive_op_labels:
#FOGI DEBUG print("##", op_label)
ga = gauge_action_matrices[op_label]
# currently `ga` is a dense matrix, if SPARSE need to update nullspace and pinv math below
#TODO: update this conditional to something more robust (same conditiona in explicitmodel.py too)
if isinstance(op_label, str) and (op_label.startswith('rho') or op_label.startswith('M')):
# Note: the "commutant" constructed in this way also includes irrelevant gauge directions,
# and for SPAM ops we know there are actually *no* local FOGI quantities and the entire
# "commutant" is irrelevant. So below we perform similar math as for gates, but don't add
# any intrinsic FOGI quantities. TODO - make this more general?
commutant = _mt.nice_nullspace(ga) # columns = *gauge* elem gen directions
complement = _mt.nice_nullspace(commutant.T) # complement of commutant - where op is faithful rep
ccomms[(op_label,)] = complement
#FOGI DEBUG print(" Skipping - SPAM, no intrinsic qtys")
continue
#Get commutant and communtant-complement spaces
commutant = _mt.nice_nullspace(ga, orthogonalize=True) # columns = *gauge* elem gen directions
assert(_mt.columns_are_orthogonal(commutant))
# Note: local/new_fogi_dirs are orthogonal but not necessarily normalized (so need to
# normalize to get inverse, but *don't* need pseudo-inverse).
local_fogi_dirs = _mt.nice_nullspace(ga.T, orthogonalize=True) # "conjugate space" to gauge action SPARSE?
#NORMALIZE FOGI DIRS to have norm 1 - based on mapping between unit-variance
# gaussian distribution of target-gateset perturbations in the usual errorgen-set-space
# to the FOGI space. The basis of the fogi directions is understood to be the basis
# of errorgen-superops arising from *un-normalized* (traditional) Pauli matrices.
ord_to_use = resolve_norm_order(local_fogi_dirs, [errorgen_coefficient_labels[op_label]], norm_order)
local_fogi_vecs = _mt.normalize_columns(local_fogi_dirs, ord=ord_to_use) # this gives us *vec*-norm we want
vector_L2_norm2s = [_np.linalg.norm(local_fogi_vecs[:, j])**2 for j in range(local_fogi_vecs.shape[1])]
local_fogi_dirs = local_fogi_vecs / _np.array(vector_L2_norm2s)[None, :] # gives *dir*-norm we want # DUAL NORM
#FOGI DEBUG print(" New intrinsic qtys = ", local_fogi_dirs.shape[1])
#assert(_np.linalg.norm(local_fogi_dirs.imag) < 1e-6) # ok for H+S but not for CPTP models
assert(_mt.columns_are_orthogonal(local_fogi_dirs)) # Not for Cnot in 2Q_XYICNOT (check?)
new_fogi_dirs = _sps.lil_matrix((num_elem_errgens, local_fogi_dirs.shape[1]), dtype=local_fogi_dirs.dtype)
new_fogi_dirs[op_errgen_indices[op_label], :] = local_fogi_dirs # "juice" this op
fogi_dirs = _sps.hstack((fogi_dirs, new_fogi_dirs.tocsc()))
#assert(_mt.columns_are_orthogonal(fogi_dirs)) # sparse version?
#LABELS
op_elemgen_labels = errorgen_coefficient_labels[op_label]
errgen_names = elem_vec_names(local_fogi_vecs, op_elemgen_labels)
errgen_names_abbrev = elem_vec_names(local_fogi_vecs, op_elemgen_labels, include_type=False)
for egname, egname_abbrev in zip(errgen_names, errgen_names_abbrev):
egname_with_op = "%s_%s" % ((("(%s)" % egname) if (' ' in egname) else egname),
op_label_abbrevs.get(op_label, str(op_label)))
fogi_meta.append({'name': egname_with_op, 'abbrev': egname_abbrev, 'r': 0,
'gaugespace_dir': None, 'opset': (op_label,)})
complement = _mt.nice_nullspace(commutant.T,
orthogonalize=True) # complement of commutant - where op is faithful rep
assert(_mt.columns_are_orthogonal(complement))
ccomms[(op_label,)] = complement
#gauge_action_for_op[op_label] = ga
#print("Commutant:"); _mt.print_mx(commutant)
#print("Names: ", errgen_names)
#print("Complement:"); _mt.print_mx(complement)
smaller_sets = [(op_label,) for op_label in primitive_op_labels]
max_size = len(primitive_op_labels)
for set_size in range(1, max_size):
larger_sets = []
num_indep_vecs_from_smaller_sets = fogi_dirs.shape[1]
for op_label in primitive_op_labels:
for existing_set in smaller_sets:
if op_label in existing_set: continue
new_set = tuple(sorted(existing_set + (op_label,)))
if new_set in larger_sets: continue
#FOGI DEBUG print("\n##", existing_set, "+", op_label)
# Merge existing set + op_label => new set of larger size
ccommA = ccomms.get(existing_set, None) # Note: commutant-complements are in *gauge* space,
ccommB = ccomms[(op_label,)] # so they're all the same dimension.
if ccommA is not None and ccommA.shape[1] > 0 and ccommB.shape[1] > 0:
# merging with an empty complement does nothing (no intersection, same ccomm)
intersection_space = _mt.intersection_space(ccommA, ccommB, use_nice_nullspace=True)
union_space = _mt.union_space(ccommA, ccommB)
#Don't orthogonalize these - instead orthogonalize fogi_dirs and find what these should be (below)
#intersection_space, _ = _np.linalg.qr(intersection_space) # gram-schmidt orthogonalize cols
#assert(_mt.columns_are_orthogonal(intersection_space)) # Not always true
if intersection_space.shape[1] > 0:
#FOGI DEBUG print(" ==> intersection space dim = ", intersection_space.shape[1])
# Then each basis vector of the intersection space defines a gauge-invariant ("fogi")
# direction via the difference between that gauge direction's action on A and B:
gauge_action = _np.concatenate([gauge_action_matrices[ol] for ol in existing_set]
+ [gauge_action_matrices[op_label]], axis=0)
n = sum([gauge_action_matrices[ol].shape[0] for ol in existing_set]) # boundary btwn A & B
# gauge trans: e => e + delta_e = e + dot(gauge_action, delta)
# e = "errorgen-set space" vector; delta = "gauge space" vector
# we want fogi_dir s.t. dot(fogi_dir.T, e) doesn't change when e transforms as above
# ==> we want fogi_dir s.t. dot(fogi_dir.T, gauge_action) == 0
# (we want dot(fogi_dir.T, gauge_action, delta) == 0 for all delta)
# There exists subspace of errgen-set space = span({dot(gauge_action, delta) for all delta}).
# We call it "gauge-shift space" == gauge-orbit of target gateset, i.e. e=vec(0) (within FOGI)
# We will construct {v_i} so each v_i is in the gauge-shift space or its orthogonal complement.
# We call components/coeffs of each v_i as FOGI or "gauge" based on where v_i lies, and
# call v_i a "FOGI direction" or "gauge direction"
# We find a set of fogi directions {f'_i} in the code that are potentially linearly dependent
# and non-orthogonal. This is ok, as we can take the entire set and just define dot(f'_i.T, e)
# to be the *component* of e along f':
# => component_i := dot(f'_i.T, e)
# If we write e as as linear combination of fogi vectors:
# e = sum_i coeff_i * f_i f_i are basis vecs, not nec. orthonormal, s.t. there exists
# a dual basis f'_i s.t. dot(f'_i.T, f_i) = dirac_ij
# so if we take a subset of the {f'_i} that are linearly dependent we can define coefficients:
# => coeff_i = dot(f'_i.T, e) # for f' in a *basis* for the complement of gauge-shift space
#Note: construction method yields FOGI-space vectors - whether these vectors are "primal"
# or "dual" is a statement about *bases* for this FOGI space, i.e. a given basis has a dual
# basis. The space is just the space - it's isomorphic to it's dual (it's a vector space).
# Observe:
# colspace(gauge_action) = gauge-shift space, and we can construct its orthogonal complement
# local_fogi_dir found by nullspace of gauge_action: dot(local_fogi.T, gauge_action) = 0
# Note: q in nullspace(gauge_action.T) => is q a valid fogi vector, i.e.
# dot(q.T, every_vec_in_gauge_shift_space) = 0 = dot(q.T, delta_e)
# = dot(q.T, gauge_action, delta) for all delta
# So dot(gauge_action.T, q) = dot(q.T, gauge_action) = 0. Thus
#Mathematically:
# let fogi_dir.T = int_vec.T * pinv(ga_A) - int_vec.T * pinv(ga_B) so that:
# dot(fogi_dir.T, gauge_action) = int_vec.T * (pinv(ga_A) - pinv(ga_B)) * gauge_action
# = (I - I) = 0
# (A,B are faithful reps of gauge on intersection space, so pinv(ga_A) * ga_A
# restricted to intersection space is I: int_spc.T * pinv(ga_A) * ga_A * int_spc == I
# (when int_spc vecs are orthonormal) and so the above redues to I - I = 0
inv_diff_gauge_action = _np.concatenate((_np.linalg.pinv(gauge_action[0:n, :], rcond=1e-7),
-_np.linalg.pinv(gauge_action[n:, :], rcond=1e-7)),
axis=1).T
#Equivalent:
#inv_diff_gauge_action = _np.concatenate((_np.linalg.pinv(gauge_action[0:n, :].T, rcond=1e-7),
# -_np.linalg.pinv(gauge_action[n:, :].T, rcond=1e-7)),
# axis=0) # same as above, b/c T commutes w/pinv (?)
if orthogonalize_relationals:
# First, lets get a "good" basis for the intersection space - one that produces
# an orthogonal set of fogi directions. Don't worry about normalization yet.
test_fogi_dirs = _np.dot(inv_diff_gauge_action, intersection_space) # dot("M", epsilons)
Q, R = _np.linalg.qr(test_fogi_dirs) # gram-schmidt orthogonalize cols
Q, R = _mt.sign_fix_qr(Q, R) # removes sign ambiguity in QR decomp (simplifies comparisons)
# test_fogi_dirs = M * epsilons = Q * R
# => want orthogonalized dirs "Q" as new dirs: Q = M * epsilons * inv(R) = M * epsilons'
intersection_space = _np.dot(intersection_space, _np.linalg.inv(R)) # a "good" basis
# start w/normalizd epsilon vecs (normalize according to norm_order, then divide by L2-norm^2
# so that the resulting intersection-space vector, after action by "M", projects the component
# of the norm_order-normalized gauge-space vector)
int_space_in_gauge_elemgen_basis = _np.dot(gauge_space.vectors, intersection_space)
ord_to_use = resolve_norm_order(int_space_in_gauge_elemgen_basis,
#intersection_space,
[gauge_space.elemgen_basis.labels],
norm_order)
#DEBUG!!! REMOVE
#print("DB: gauge norm_order to use = ", ord_to_use)
#if list(ord_to_use) == [2, 2, 2, 2, 2, 2, 1, 1, 1]:
# import bpdb; bpdb.set_trace()
# print("HERE")
int_vecs_in_geb = _mt.normalize_columns(int_space_in_gauge_elemgen_basis, ord=ord_to_use)
int_vecs = _np.linalg.pinv(gauge_space.vectors) @ int_vecs_in_geb
vector_L2_norm2s = [_np.linalg.norm(int_vecs[:, j])**2 for j in range(int_vecs.shape[1])]
intersection_space = int_vecs / _np.array(vector_L2_norm2s)[None, :] # DUAL NORM
local_fogi_dirs = _np.dot(inv_diff_gauge_action, intersection_space) # dot("M", epsilons)
#assert(_np.linalg.norm(local_fogi_dirs.imag) < 1e-6) # ok for H+S but not for CPTP models
#Note: at this point `local_fogi_dirs` vectors are gauge-space-normalized, not numpy-norm-1
if orthogonalize_relationals:
assert(_mt.columns_are_orthogonal(local_fogi_dirs)) # true if we orthogonalize above
#NORMALIZATION:
# There are two normalizations relevant to relational fogi directions:
# 1) we normalize the would-be fogi vectors (those that would be prefixed by components in
# a linear expansion if the fogi directions were an orthogonal basis) to 1 using
# the `norm_order` norm. The fogi directions are then normalized so
# numpy.dot(dir, vec) = 1.0, i.e. so their L2 norm = 1/L2-norm2-of-norm_order-normalized-vec.
# => set dir = vec / L2(vec)**2 so, if L2(vec)=x, then L2(dir)=1/x and dot(dir,vec) = 1.0
# (Recall: The fogi component is defined as dot(fogi_dir.T, e)).
# 2) gauge vectors epsilon are also chosen to be normalized to 1 within gauge space.
# Each epsilon in the intersection space gives rise via the "M" action
# (inv_diff_gauge_action) to a fogi vector of norm 1/r so that
# fogi_dir = r * dot(M, epsilon) = r * dot(inv_diff_gauge_action, int_vec)
# We keep track of this 'r' value as a way of converting between the gauge-space-normalized
# FOGI direction to the errgen-space-normalized version of it.
# The errgen-space-normalized fogi vector (in fogi_dirs) defines the "FOGI component",
# whereas the gauge-space-normalized version defines the "FOGI gauge angle"
# theta = dot(gauge_normd_fogi_dir.T, e) = dot( dot(M, epsilon).T, e) = dot(fogi_dir.T / r, e)
# = component / r
norm_order_array = resolve_norm_order(
local_fogi_dirs,
[errorgen_coefficient_labels[ol] for ol in existing_set + (op_label,)],
norm_order)
try:
assert(_np.linalg.norm(_np.dot(gauge_action.T, local_fogi_dirs)) < 1e-8)
except:
#TODO: REMOVE !!!!!!!!!!!!!!!!!!!!!!!!!!!
g = intersection_space
A = gauge_action[0:n, :]
B = gauge_action[n:, :]
Na = _mt.nice_nullspace(A, orthogonalize=True)
Nb = _mt.nice_nullspace(B, orthogonalize=True)
pinvA = _np.linalg.pinv(A, rcond=1e-7)
pinvB = _np.linalg.pinv(B, rcond=1e-7)
testA = pinvA @ A
testA2 = intersection_space.T @ testA @ intersection_space
testB = pinvB @ B
testB2 = intersection_space.T @ testB @ intersection_space
gtestA = testA @ g
gtestB = testB @ g
import scipy.linalg as _spl
tg = _mt.intersection_space(ccommA, ccommB, use_nice_nullspace=False)
ttg = _mt.intersection_space(ccommA, ccommB, use_nice_nullspace=True)
print("Hermitian")
print(_np.linalg.norm(testA - testA.T.conjugate()))
print(_np.linalg.norm(testB - testB.T.conjugate()))
print("Nullspace")
print(_np.linalg.norm(g.T.conjugate() @ Na))
print(_np.linalg.norm(g.T.conjugate() @ Nb))
print("pinvA * A acts as identity on g")
print(_np.linalg.norm(g.T @ testA - g.T))
print(_np.linalg.norm(g.T @ testB - g.T))
print(_np.linalg.norm(testA @ g - g))
print(_np.linalg.norm(testB @ g - g))
print("g in span")
print(_np.linalg.matrix_rank(_np.concatenate((ccommA, g), axis=1)))
print(_np.linalg.matrix_rank(_np.concatenate((ccommB, g), axis=1)))
print("tg in span")
print(_np.linalg.matrix_rank(_np.concatenate((ccommA, tg), axis=1)))
print(_np.linalg.matrix_rank(_np.concatenate((ccommB, tg), axis=1)))
import bpdb; bpdb.set_trace()
print("ERROR!")
# transpose => dot(local_fogi_dirs.T, gauge_action) = 0
# = int_spc.T * [ pinv_gA -pinv_gB ] * [[ga] [gB]]
# = int_spc.T * (pinv_gA * gA - pinv_gB * gB) = 0 b/c int_vec lies in "support" of A & B,
# i.e. int_spc.T * (pinv_gA * gA) * int_spc == I and similar with B, so get I - I = 0
new_fogi_dirs = _sps.lil_matrix((num_elem_errgens, local_fogi_dirs.shape[1]),
dtype=local_fogi_dirs.dtype); off = 0
for ol in existing_set + (op_label,): # NOT new_set here b/c concat order above
n = len(errorgen_coefficient_labels[ol])
new_fogi_dirs[op_errgen_indices[ol], :] = local_fogi_dirs[off:off + n, :]; off += n
new_fogi_dirs = new_fogi_dirs.tocsc()
# figure out which directions are independent
indep_cols = _mt.independent_columns(new_fogi_dirs, fogi_dirs)
#FOGI DEBUG print(" ==> %d independent columns" % len(indep_cols))
if dependent_fogi_action == "drop":
dep_cols_to_add = []
elif dependent_fogi_action == "mark":
#Still add, as dependent fogi quantities, those that are independent of
# all the smaller-size op sets but dependent only on other sets of the current size.
smallset_indep_cols = _mt.independent_columns(
new_fogi_dirs, fogi_dirs[:, 0:num_indep_vecs_from_smaller_sets])
indep_cols_set = set(indep_cols) # just for faster lookup
dep_cols_to_add = [i for i in smallset_indep_cols if i not in indep_cols_set]
else:
raise ValueError("Invalid `dependent_fogi_action` value: %s" % str(dependent_fogi_action))
# add new_fogi_dirs[:, indep_cols] to fogi_dirs w/meta data
fogi_dirs = add_relational_fogi_dirs(new_fogi_dirs[:, indep_cols],
_np.take(int_vecs, indep_cols, axis=1),
_np.take(intersection_space, indep_cols, axis=1),
fogi_dirs, fogi_meta, existing_set, op_label, new_set,
norm_order_array[indep_cols])
# add new_fogi_dirs[:, dep_cols_to_add] to dep_fogi_dirs w/meta data
dep_fogi_dirs = add_relational_fogi_dirs(new_fogi_dirs[:, dep_cols_to_add],
_np.take(int_vecs, dep_cols_to_add, axis=1),
_np.take(intersection_space, dep_cols_to_add, axis=1),
dep_fogi_dirs, dep_fogi_meta, existing_set,
op_label, new_set, norm_order_array[dep_cols_to_add])
#if dependent_fogi_action == "drop": # we could construct these, but would make fogi qtys messy
# assert(_mt.columns_are_orthogonal(fogi_dirs))
#print("Fogi vecs:\n"); _mt.print_mx(local_fogi_dirs)
#print("Ham Intersection names: ", intersection_names)
ccomms[new_set] = union_space
#print("Complement:\n"); _mt.print_mx(union_space)
larger_sets.append(new_set)
smaller_sets = larger_sets
#big_gauge_action = _np.concatenate([other_gauge_action[ol] for ol in primitive_op_labels], axis=0) # DEBUG
#print("Fogi directions:\n"); _mt.print_mx(fogi_dirs, width=5, prec=1)
#print("Names = \n", '\n'.join(["%d: %s" % (i, v) for i, v in enumerate(fogi_names)]))
#print("Rank = ", _np.linalg.matrix_rank(fogi_dirs))
#Convert to real matrices if possible (otherwise we can get pinv or nullspace being complex when it doesn't
# need to be, and this causes, e.g. an attempt to set imaginary Hamiltonian coefficients of ops)
if _spsl.norm(fogi_dirs.imag) < 1e-6:
fogi_dirs = fogi_dirs.real
if _spsl.norm(dep_fogi_dirs.imag) < 1e-6:
dep_fogi_dirs = dep_fogi_dirs.real
return (fogi_dirs, fogi_meta, dep_fogi_dirs, dep_fogi_meta)
#def create_fogi_dir_labels(fogi_opsets, fogi_dirs, fogi_rs, fogi_gaugespace_dirs, errorgen_coefficients):
#
# fogi_names = []
# fogi_abbrev_names = []
#
# # Note: fogi_dirs is a 2D array, so .T to iterate over cols, whereas fogi_gaugespace_dirs
# # is a list of vectors, so just iterating is fine.
# for opset, fogi_dir, fogi_epsilon in zip(fogi_opsets, fogi_dirs.T, fogi_gaugespace_dirs):
#
# if len(opset) == 1: # Intrinsic quantity
# assert(fogi_epsilon is None)
# op_elemgen_labels = errorgen_coefficient_labels[op_label]
# errgen_name = elem_vec_name(fogi_dir, op_elemgen_labels)
# errgen_names_abbrev = elem_vec_names(local_fogi_dirs, op_elemgen_labels, include_type=False)
# fogi_names.extend(["%s_%s" % ((("(%s)" % egname) if (' ' in egname) else egname),
# op_label_abbrevs.get(op_label, str(op_label)))
# for egname in errgen_names])
# fogi_abbrev_names.extend(errgen_names_abbrev)
#
# intersection_space_to_add = _np.take(intersection_space, rel_cols_to_add, axis=1)
# #intersection_space_to_add = _np.dot(gauge_linear_combos, indep_intersection_space) \
# # if (gauge_linear_combos is not None) else intersection_space_to_add
#
#
#
#
# intersection_names = elem_vec_names(intersection_space_to_add, gauge_elemgen_labels)
# intersection_names_abbrev = elem_vec_names(intersection_space_to_add, gauge_elemgen_labels,
# include_type=False)
# fogi_names.extend(["ga(%s)_%s - ga(%s)_%s" % (
# iname, "|".join([op_label_abbrevs.get(l, str(l)) for l in existing_set]),
# iname, op_label_abbrevs.get(op_label, str(op_label))) for iname in intersection_names])
# fogi_abbrev_names.extend(["ga(%s)" % iname for iname in intersection_names_abbrev])
def compute_maximum_relational_errors(primitive_op_labels, errorgen_coefficients, gauge_action_matrices,
errorgen_coefficient_bases_by_op, gauge_basis, model_dim):
""" TODO: docstring """
gaugeSpaceDim = len(gauge_basis)
errorgen_vec = {}
for op_label in primitive_op_labels:
errgen_dict = errorgen_coefficients[op_label]
errorgen_vec[op_label] = _np.array([errgen_dict.get(eglbl, 0)
for eglbl in errorgen_coefficient_bases_by_op[op_label].labels])
def fix_gauge_using_op(op_label, allowed_gauge_directions, available_op_labels, running_best_gauge_vec,
best_gauge_vecs, debug, op_label_to_compute_max_for):
if op_label is not None: # initial iteration gives 'None' as op_label to kick things off
ga = gauge_action_matrices[op_label]
# get gauge directions that commute with gate:
commutant = _mt.nullspace(ga) # columns = *gauge* elem gen directions - these can't be fixed by this op
assert(_mt.columns_are_orthonormal(commutant))
#complement = _mt.nullspace(commutant.T) # complement of commutant - where op is faithful rep
# take intersection btwn allowed_gauge_directions and complement
best_gauge_dir = - _np.dot(_np.linalg.pinv(ga), errorgen_vec[op_label])
coeffs = _np.dot(_np.linalg.pinv(allowed_gauge_directions), best_gauge_dir) # project onto Q (allowed dirs)
projected_best_gauge_dir = _np.dot(allowed_gauge_directions, coeffs)
# add projected vec to running "best_gauge_vector"
running_best_gauge_vec = running_best_gauge_vec.copy()
running_best_gauge_vec += projected_best_gauge_dir
#update allowed gauge directions by taking intersection with commutant
allowed_gauge_directions = _mt.intersection_space(allowed_gauge_directions, commutant,
use_nice_nullspace=False)
assert(_mt.columns_are_orthogonal(allowed_gauge_directions))
for i in range(allowed_gauge_directions.shape[1]):
allowed_gauge_directions[:, i] /= _np.linalg.norm(allowed_gauge_directions[:, i])
assert(_mt.columns_are_orthonormal(allowed_gauge_directions))
available_op_labels.remove(op_label)
if allowed_gauge_directions.shape[1] > 0: # if there are still directions to fix, recurse
assert(len(available_op_labels) > 0), "There are still unfixed gauge directions but we've run out of gates!"
for oplbl in available_op_labels:
fix_gauge_using_op(oplbl, allowed_gauge_directions, available_op_labels.copy(),
running_best_gauge_vec, best_gauge_vecs, debug + [oplbl],
op_label_to_compute_max_for)
else:
# we've entirely fixed the gauge - running_best_gauge_vec is actually a best gauge vector now.
errgen_shift = _np.dot(gauge_action_matrices[op_label_to_compute_max_for], running_best_gauge_vec)
#commutant = _mt.nullspace(ga) # columns = *gauge* elem gen directions - these can't be fixed by this op
#assert(_mt.columns_are_orthonormal(commutant))
#complement = _mt.nullspace(commutant.T) # complement of commutant - where op is faithful rep
ga = gauge_action_matrices[op_label_to_compute_max_for]
errgen_vec = errorgen_vec[op_label_to_compute_max_for] + errgen_shift
errgen_vec = _np.dot(_np.dot(ga, _np.linalg.pinv(ga)), errgen_vec)
#jangle = _mt.jamiolkowski_angle(_create_errgen_op(errgen_vec, gauge_basis_mxs))
#FOGI DEBUG print("From ", debug, " jangle = ", jangle)
best_gauge_vecs.append(running_best_gauge_vec)
def _create_errgen_op(vec, list_of_mxs):
return sum([c * mx for c, mx in zip(vec, list_of_mxs)])
from ..baseobjs import Basis as _Basis
ret = {}
normalized_pauli_basis = _Basis.cast('pp', model_dim)
scale = model_dim**(0.25) # to change to standard pauli-product matrices
gauge_basis_mxs = [mx * scale for mx in normalized_pauli_basis.elements[1:]]
for op_label_to_compute_max_for in primitive_op_labels:
#FOGI DEBUG print("Computing for", op_label_to_compute_max_for)
running_gauge_vec = _np.zeros(gaugeSpaceDim, 'd')
initial_allowed_gauge_directions = _np.identity(gaugeSpaceDim, 'd')
resulting_best_gauge_vecs = []
available_labels = set(primitive_op_labels)
#available_labels.remove(op_label_to_compute_max_for)
fix_gauge_using_op(None, initial_allowed_gauge_directions, available_labels,
running_gauge_vec, resulting_best_gauge_vecs, debug=[],
op_label_to_compute_max_for=op_label_to_compute_max_for)
jamiol_angles = []
ga = gauge_action_matrices[op_label_to_compute_max_for]
projector = _np.dot(ga, _np.linalg.pinv(ga))
for gauge_vec in resulting_best_gauge_vecs:
errgen_shift = _np.dot(gauge_action_matrices[op_label_to_compute_max_for], gauge_vec)
errgen_vec = errorgen_vec[op_label_to_compute_max_for] + errgen_shift
errgen_vec = _np.dot(projector, errgen_vec) # project onto non-local space
jamiol_angles.append(_mt.jamiolkowski_angle(_create_errgen_op(errgen_vec, gauge_basis_mxs)))
max_relational_jangle = max(jamiol_angles)
#FOGI DEBUG print(max_relational_jangle)
ret[op_label_to_compute_max_for] = max_relational_jangle
return ret
#An alternative but inferior algorithm for constructing FOGI quantities: Keep around for checking/reference or REMOVE?
#def _compute_fogi_via_nullspaces(self, primitive_op_labels, ham_basis, other_basis, other_mode="all",
# ham_gauge_linear_combos=None, other_gauge_linear_combos=None,
# op_label_abbrevs=None, reduce_to_model_space=True):
# num_ham_elem_errgens = (len(ham_basis) - 1)
# num_other_elem_errgens = (len(other_basis) - 1)**2 if other_mode == "all" else (len(other_basis) - 1)
# ham_elem_labels = [('H', bel) for bel in ham_basis.labels[1:]]
# other_elem_labels = [('S', bel) for bel in other_basis.labels[1:]] if other_mode != "all" else \
# [('S', bel1, bel2) for bel1 in other_basis.labels[1:] for bel2 in other_basis.labels[1:]]
# assert(len(ham_elem_labels) == num_ham_elem_errgens)
# assert(len(other_elem_labels) == num_other_elem_errgens)
#
# #Get lists of the present (existing within the model) labels for each operation
# ham_labels_for_op = {op_label: ham_elem_labels[:] for op_label in primitive_op_labels} # COPY lists!
# other_labels_for_op = {op_label: other_elem_labels[:] for op_label in primitive_op_labels} # ditto
# if reduce_to_model_space:
# for op_label in primitive_op_labels:
# op = self.operations[op_label]
# lbls = op.errorgen_coefficient_labels()
# present_ham_elem_lbls = set(filter(lambda lbl: lbl[0] == 'H', lbls))
# present_other_elem_lbls = set(filter(lambda lbl: lbl[0] == 'S', lbls))
#
# disallowed_ham_space_labels = set(ham_elem_labels) - present_ham_elem_lbls
# disallowed_row_indices = [ham_elem_labels.index(disallowed_lbl)
# for disallowed_lbl in disallowed_ham_space_labels]
# for i in sorted(disallowed_row_indices, reverse=True):
# del ham_labels_for_op[op_label][i]
#
# disallowed_other_space_labels = set(other_elem_labels) - present_other_elem_lbls
# disallowed_row_indices = [other_elem_labels.index(disallowed_lbl)
# for disallowed_lbl in disallowed_other_space_labels]
# for i in sorted(disallowed_row_indices, reverse=True):
# del other_labels_for_op[op_label][i]
#
# #Step 1: construct nullspaces associated with sets of operations
# ham_nullspaces = {}
# other_nullspaces = {}
# max_size = len(primitive_op_labels)
# for set_size in range(1, max_size + 1):
# ham_nullspaces[set_size] = {} # dict mapping operation-sets of `set_size` to nullspaces
# other_nullspaces[set_size] = {}
#
# for op_set in _itertools.combinations(primitive_op_labels, set_size):
# #print(op_set)
# ham_gauge_action_mxs = []
# other_gauge_action_mxs = []
# ham_rows_by_op = {}; h_off = 0
# other_rows_by_op = {}; o_off = 0
# for op_label in op_set: # Note: "ga" stands for "gauge action" in variable names below
# op = self.operations[op_label]
# if isinstance(op, _op.LindbladOp):
# op_mx = op.unitary_postfactor.to_dense()
# else:
# assert(False), "STOP - you probably don't want to do this!"
# op_mx = op.to_dense()
# U = _bt.change_basis(op_mx, self.basis, 'std')
# ham_ga = _gt.first_order_ham_gauge_action_matrix(U, ham_basis)
# other_ga = _gt.first_order_other_gauge_action_matrix(U, other_basis, other_mode)
#
# if ham_gauge_linear_combos is not None:
# ham_ga = _np.dot(ham_ga, ham_gauge_linear_combos)
# if other_gauge_linear_combos is not None:
# other_ga = _np.dot(other_ga, other_gauge_linear_combos)
#
# ham_gauge_action_mxs.append(ham_ga)
# other_gauge_action_mxs.append(other_ga)
# reduced_ham_nrows = len(ham_labels_for_op[op_label]) # ham_ga.shape[0] when unrestricted
# reduced_other_nrows = len(other_labels_for_op[op_label]) # other_ga.shape[0] when unrestricted
# ham_rows_by_op[op_label] = slice(h_off, h_off + reduced_ham_nrows); h_off += reduced_ham_nrows
# other_rows_by_op[op_label] = slice(o_off, o_off + reduced_other_nrows); o_off += reduced_other_nrows
# assert(ham_ga.shape[0] == num_ham_elem_errgens)
# assert(other_ga.shape[0] == num_other_elem_errgens)
#
# #Stack matrices to form "base" gauge action matrix for op_set
# ham_ga_mx = _np.concatenate(ham_gauge_action_mxs, axis=0)
# other_ga_mx = _np.concatenate(other_gauge_action_mxs, axis=0)
#
# # Intersect gauge action with the space of elementary errorgens present in the model.
# # We may need to eliminate some rows of X_ga matrices, and (only) keep linear combos
# # of the columns that are zero on these rows.
# present_ham_elem_lbls = set()
# present_other_elem_lbls = set()
# for op_label in op_set:
# op = self.operations[op_label]
# lbls = op.errorgen_coefficient_labels() # length num_coeffs
# present_ham_elem_lbls.update([(op_label, lbl) for lbl in lbls if lbl[0] == 'H'])
# present_other_elem_lbls.update([(op_label, lbl) for lbl in lbls if lbl[0] == 'S'])
#
# full_ham_elem_labels = [(op_label, elem_lbl) for op_label in op_set
# for elem_lbl in ham_elem_labels]
# assert(present_ham_elem_lbls.issubset(full_ham_elem_labels)), \
# "The given space of hamiltonian elementary gauge-gens must encompass all those in model ops!"
# disallowed_ham_space_labels = set(full_ham_elem_labels) - present_ham_elem_lbls
# disallowed_row_indices = [full_ham_elem_labels.index(disallowed_lbl)
# for disallowed_lbl in disallowed_ham_space_labels]
#
# if reduce_to_model_space and len(disallowed_row_indices) > 0:
# #disallowed_rows = _np.take(ham_ga_mx, disallowed_row_indices, axis=0)
# #allowed_linear_combos = _mt.nice_nullspace(disallowed_rows, tol=1e-4)
# #ham_ga_mx = _np.dot(ham_ga_mx, allowed_linear_combos)
# ham_ga_mx = _np.delete(ham_ga_mx, disallowed_row_indices, axis=0)
#
# full_other_elem_labels = [(op_label, elem_lbl) for op_label in op_set
# for elem_lbl in other_elem_labels]
# assert(present_other_elem_lbls.issubset(full_other_elem_labels)), \
# "The given space of 'other' elementary gauge-gens must encompass all those in model ops!"
# disallowed_other_space_labels = set(full_other_elem_labels) - present_other_elem_lbls
# disallowed_row_indices = [full_other_elem_labels.index(disallowed_lbl)
# for disallowed_lbl in disallowed_other_space_labels]
#
# if reduce_to_model_space and len(disallowed_row_indices) > 0:
# #disallowed_rows = _np.take(other_ga_mx, disallowed_row_indices, axis=0)
# #allowed_linear_combos = _mt.nice_nullspace(disallowed_rows, tol=1e-4)
# #other_ga_mx = _np.dot(other_ga_mx, allowed_linear_combos)
# other_ga_mx = _np.delete(other_ga_mx, disallowed_row_indices, axis=0)
#
# #Add all known (already tabulated) nullspace directions so that we avoid getting them again
# # when we compute the nullspace of the gauge action matrix below.
# for previous_size in range(1, set_size + 1): # include current size!
# for previous_op_set, (nullsp, previous_rows) in ham_nullspaces[previous_size].items():
# padded_nullsp = _np.zeros((ham_ga_mx.shape[0], nullsp.shape[1]), 'd')
# for op in previous_op_set:
# if op not in ham_rows_by_op: continue
# padded_nullsp[ham_rows_by_op[op], :] = nullsp[previous_rows[op], :]
# ham_ga_mx = _np.concatenate((ham_ga_mx, padded_nullsp), axis=1)
#
# for previous_op_set, (nullsp, previous_rows) in other_nullspaces[previous_size].items():
# padded_nullsp = _np.zeros((other_ga_mx.shape[0], nullsp.shape[1]), other_ga_mx.dtype)
# for op in previous_op_set:
# if op not in other_rows_by_op: continue
# padded_nullsp[other_rows_by_op[op], :] = nullsp[previous_rows[op], :]
# other_ga_mx = _np.concatenate((other_ga_mx, padded_nullsp), axis=1)
#