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Merged
Parent: add optional probs parameter to log_likelihood functions. Using this …
merged 4 commits into from
Oct 24, 2025
Merged

Merge bugfix into develop (resolve failed auto-merge) #675

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3423dc1
Fixes a bug in DataSet count retrieval and adds unit tests.
enielse Oct 7, 2025
bb89527
Fixes a bug in DataSet count retrieval and adds unit tests. (#663)
coreyostrove Oct 8, 2025
839884b
`lsvec` Shared Memory Race Condition (#674)
coreyostrove Oct 24, 2025
b5feea1
Ensure that gauge optimization incorporates instruments (#672)
rileyjmurray Oct 24, 2025
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45 changes: 25 additions & 20 deletions pygsti/algorithms/gaugeopt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,8 @@
TrivialGaugeGroupElement as _TrivialGaugeGroupElement,
GaugeGroupElement as _GaugeGroupElement
)
from pygsti.models import ExplicitOpModel
from pygsti.modelmembers.operations import LinearOperator as _LinearOperator

from typing import Callable, Union, Optional

Expand Down Expand Up @@ -360,8 +362,10 @@ def _call_jacobian_fn(gauge_group_el_vec):

GGElJacobian = Union[None, Callable[[_GaugeGroupElement], _np.ndarray]]

LabelLike = Union[str, _baseobjs.Label]

def _create_objective_fn(model, target_model, item_weights: Optional[dict[str,float]]=None,

def _create_objective_fn(model, target_model, item_weights: Optional[dict[LabelLike, float]]=None,
cptp_penalty_factor: float=0.0, spam_penalty_factor: float=0.0,
gates_metric="frobenius", spam_metric="frobenius",
method=None, comm=None, check_jac=False, n_leak=0) -> tuple[GGElObjective, GGElJacobian]:
Expand Down Expand Up @@ -625,8 +629,14 @@ def _mock_objective_fn(v):
# ^ It would be equivalent to set this to a pair of IdentityOperator objects.

def _objective_fn(gauge_group_el, oob_check):
mdl = _transform_with_oob_check(model, gauge_group_el, oob_check)
ret = 0
mdl : ExplicitOpModel = _transform_with_oob_check(model, gauge_group_el, oob_check)
mdl_ops : dict[_baseobjs.Label, _LinearOperator] = mdl._excalc().operations
tgt_ops : dict[_baseobjs.Label, _LinearOperator] = dict()
if target_model is not None:
tgt_ops.update(target_model._excalc().operations)
# ^ Use these dicts instead of mdl.operations and target_model.operations,
# since these dicts are updated to include instruments.
ret = 0.0

if cptp_penalty_factor > 0:
mdl.basis = mxBasis # set basis for jamiolkowski iso
Expand All @@ -645,30 +655,27 @@ def _objective_fn(gauge_group_el, oob_check):
if spam_metric == gates_metric:
val = mdl.frobeniusdist(target_model, transform_mx_arg, item_weights)
else:
wts = item_weights.copy()
wts['spam'] = 0.0
for k in wts:
if k in mdl.preps or k in mdl.povms:
wts[k] = 0.0
val = mdl.frobeniusdist(target_model, transform_mx_arg, wts, n_leak)
non_gates = {'spam'}.union(set(mdl.preps)).union(set(mdl.povms))
temp_wts = {k: (0.0 if k in non_gates else v) for (k, v) in item_weights.items()}
val = mdl.frobeniusdist(target_model, transform_mx_arg, temp_wts)
if "squared" in gates_metric:
val = val ** 2
ret += val

elif gates_metric == "fidelity":
# If n_leak==0, then subspace_entanglement_fidelity is just entanglement_fidelity
for opLbl in mdl.operations:
for opLbl in mdl_ops:
wt = item_weights.get(opLbl, opWeight)
top = target_model.operations[opLbl].to_dense()
mop = mdl.operations[opLbl].to_dense()
top = tgt_ops[opLbl].to_dense()
mop = mdl_ops[opLbl].to_dense()
ret += wt * (1.0 - _tools.subspace_entanglement_fidelity(top, mop, mxBasis, n_leak))**2

elif gates_metric == "tracedist":
# If n_leak==0, then subspace_jtracedist is just jtracedist.
for opLbl in mdl.operations:
for opLbl in mdl_ops:
wt = item_weights.get(opLbl, opWeight)
top = target_model.operations[opLbl].to_dense()
mop = mdl.operations[opLbl].to_dense()
top = tgt_ops[opLbl].to_dense()
mop = mdl_ops[opLbl].to_dense()
ret += wt * _tools.subspace_jtracedist(top, mop, mxBasis, n_leak)

else:
Expand All @@ -680,11 +687,9 @@ def _objective_fn(gauge_group_el, oob_check):

if "frobenius" in spam_metric:
# SPAM and gates can have different choices for squared vs non-squared.
wts = item_weights.copy(); wts['gates'] = 0.0
for k in wts:
if k in mdl.operations or k in mdl.instruments:
wts[k] = 0.0
val = mdl.frobeniusdist(target_model, transform_mx_arg, wts)
non_spam = {'gates'}.union(set(mdl_ops)) # use mdl_ops, not mdl.operations!
temp_wts = {k: (0.0 if k in non_spam else v) for (k, v) in item_weights.items()}
val = mdl.frobeniusdist(target_model, transform_mx_arg, temp_wts)
if "squared" in spam_metric:
val = val ** 2
ret += val
Expand Down
6 changes: 3 additions & 3 deletions pygsti/data/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -575,18 +575,18 @@ def _get_counts(self, timestamp=None, all_outcomes=False):
cntDict.setitem_unsafe(ol, cnt)
else:
for ol, i in self.dataset.olIndex.items():
inds = oli_tslc[oli_tslc == i]
inds = _np.where(oli_tslc == i)[0]
if len(inds) > 0 or all_outcomes:
cntDict.setitem_unsafe(ol, float(sum(self.reps[tslc][inds])))
else:
if self.reps is None:
for ol_index in oli_tslc:
ol = self.dataset.ol[ol_index]
cntDict.setitem_unsafe(ol, 1.0 + cntDict.getitem_unsafe(ol, 0.0))
cntDict.setitem_unsafe(ol, float(1.0 + cntDict.getitem_unsafe(ol, 0.0)))
else:
for ol_index, reps in zip(oli_tslc, self.reps[tslc]):
ol = self.dataset.ol[ol_index]
cntDict.setitem_unsafe(ol, reps + cntDict.getitem_unsafe(ol, 0.0))
cntDict.setitem_unsafe(ol, float(reps + cntDict.getitem_unsafe(ol, 0.0)))

return cntDict

Expand Down
20 changes: 9 additions & 11 deletions pygsti/modelmembers/operations/linearop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -506,9 +506,8 @@ def jtracedist(self, other_op, transform=None, inv_transform=None):
if transform is None and inv_transform is None:
return _ot.jtracedist(self.to_dense("minimal"), other_op.to_dense("minimal"))
else:
return _ot.jtracedist(_np.dot(
inv_transform, _np.dot(self.to_dense("minimal"), transform)),
other_op.to_dense("minimal"))
arg = inv_transform @ self.to_dense("minimal") @ transform
return _ot.jtracedist(arg, other_op.to_dense("minimal"))

def diamonddist(self, other_op, transform=None, inv_transform=None):
"""
Expand All @@ -535,9 +534,8 @@ def diamonddist(self, other_op, transform=None, inv_transform=None):
if transform is None and inv_transform is None:
return _ot.diamonddist(self.to_dense("minimal"), other_op.to_dense("minimal"))
else:
return _ot.diamonddist(_np.dot(
inv_transform, _np.dot(self.to_dense("minimal"), transform)),
other_op.to_dense("minimal"))
arg = inv_transform @ self.to_dense("minimal") @ transform
return _ot.diamonddist(arg, other_op.to_dense("minimal"))

def transform_inplace(self, s):
"""
Expand All @@ -563,7 +561,7 @@ def transform_inplace(self, s):
"""
Smx = s.transform_matrix
Si = s.transform_matrix_inverse
self.set_dense(_np.dot(Si, _np.dot(self.to_dense("minimal"), Smx)))
self.set_dense(Si @ self.to_dense("minimal") @ Smx)

def spam_transform_inplace(self, s, typ):
"""
Expand Down Expand Up @@ -591,9 +589,9 @@ def spam_transform_inplace(self, s, typ):
None
"""
if typ == 'prep':
self.set_dense(_np.dot(s.transform_matrix_inverse, self.to_dense("minimal")))
self.set_dense(s.transform_matrix_inverse @ self.to_dense("minimal"))
elif typ == 'effect':
self.set_dense(_np.dot(self.to_dense("minimal"), s.transform_matrix))
self.set_dense(self.to_dense("minimal") @ s.transform_matrix)
else:
raise ValueError("Invalid `typ` argument: %s" % typ)

Expand Down Expand Up @@ -627,7 +625,7 @@ def depolarize(self, amount):
else:
assert(len(amount) == self.dim - 1)
D = _np.diag([1] + list(1.0 - _np.array(amount, 'd')))
self.set_dense(_np.dot(D, self.to_dense("minimal")))
self.set_dense(D @ self.to_dense("minimal"))

def rotate(self, amount, mx_basis="gm"):
"""
Expand Down Expand Up @@ -658,7 +656,7 @@ def rotate(self, amount, mx_basis="gm"):
None
"""
rotnMx = _ot.rotation_gate_mx(amount, mx_basis)
self.set_dense(_np.dot(rotnMx, self.to_dense("minimal")))
self.set_dense(rotnMx @ self.to_dense("minimal"))

def deriv_wrt_params(self, wrt_filter=None):
"""
Expand Down
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