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@rileyjmurray
rileyjmurray committed Nov 21, 2024
1 parent a138b7b commit 1f49e43
Showing 1 changed file with 68 additions and 96 deletions.
164 changes: 68 additions & 96 deletions pygsti/algorithms/gaugeopt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -619,6 +619,8 @@ def _mock_objective_fn(v):

assert gates_metric != "frobeniustt"
assert spam_metric != "frobeniustt"
assert spam_metric == gates_metric
metric = spam_metric
# assert spam_metric == gates_metric
# ^ Erik and Corey said these are rarely used. I've removed support for
# them in this codepath (non-LS optimizer) in order to make it easier to
Expand All @@ -639,103 +641,73 @@ def _objective_fn(gauge_group_el, oob_check):
spamPenaltyVec = _spam_penalty(mdl, spam_penalty_factor, mdl.basis)
ret += _np.sum(spamPenaltyVec)

if target_model is not None:
"""
Leakage-aware metric supported, per implementation in mdl.frobeniusdist.
Refer to how mdl.frobeniusdist handles the case when transform_mx_arg
is a tuple in order to understand how the leakage-aware metric is defined.
Idea: raise an error if mxBasis isn't leakage-friendly.
PROBLEM: if we're deep in the code then we end up needing to be
working in a basis that has the identity matrix as its
first element. The leakage-friendly basis doesn't even
have the identity matrix as an element, let alone the first element
TODO: dig into function calls below and see where we can
access the mxBasis object. (I'm sure we can.)
Looks like it isn't accessible within ExplicitOpModelCalc objects.
It's most definitely available in ExplicitOpModel.
"""
if "frobenius" in gates_metric:
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)
if "squared" in gates_metric:
val = val ** 2
ret += val

elif gates_metric == "fidelity":
# Leakage-aware metric supported, using leaky_entanglement_fidelity.
for opLbl in mdl.operations:
wt = item_weights.get(opLbl, opWeight)
top = target_model.operations[opLbl].to_dense()
mop = mdl.operations[opLbl].to_dense()
ret += wt * (1.0 - _tools.leaky_entanglement_fidelity(top, mop, mxBasis, n_leak))**2

elif gates_metric == "tracedist":
# Leakage-aware metric supported, using leaky_jtracedist.
for opLbl in mdl.operations:
wt = item_weights.get(opLbl, opWeight)
top = target_model.operations[opLbl].to_dense()
mop = mdl.operations[opLbl].to_dense()
ret += wt * _tools.leaky_jtracedist(top, mop, mxBasis, n_leak)

else: raise ValueError("Invalid gates_metric: %s" % gates_metric)

if "frobenius" in spam_metric and gates_metric == spam_metric:
# We already handled SPAM error in this case. Just return.
return ret

elif "frobenius" in spam_metric:
# Leakage-aware metric supported in principle via implementation in
# mdl.frobeniusdist (check implementation to see how it handles the
# case when transform_mx_arg is a tuple).
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)
if "squared" in spam_metric:
val = val ** 2
ret += val

elif spam_metric == "fidelity":
# Leakage-aware metrics NOT available
for preplabel, m_prep in mdl.preps.items():
wt = item_weights.get(preplabel, spamWeight)
rhoMx1 = _tools.vec_to_stdmx(m_prep.to_dense(), mxBasis)
t_prep = target_model.preps[preplabel]
rhoMx2 = _tools.vec_to_stdmx(t_prep.to_dense(), mxBasis)
ret += wt * (1.0 - _tools.fidelity(rhoMx1, rhoMx2))**2

for povmlabel in mdl.povms.keys():
wt = item_weights.get(povmlabel, spamWeight)
fidelity = _tools.povm_fidelity(mdl, target_model, povmlabel)
ret += wt * (1.0 - fidelity)**2

elif spam_metric == "tracedist":
# Leakage-aware metrics NOT available.
for preplabel, m_prep in mdl.preps.items():
wt = item_weights.get(preplabel, spamWeight)
rhoMx1 = _tools.vec_to_stdmx(m_prep.to_dense(), mxBasis)
t_prep = target_model.preps[preplabel]
rhoMx2 = _tools.vec_to_stdmx(t_prep.to_dense(), mxBasis)
ret += wt * _tools.tracedist(rhoMx1, rhoMx2)

for povmlabel in mdl.povms.keys():
wt = item_weights.get(povmlabel, spamWeight)
ret += wt * _tools.povm_jtracedist(mdl, target_model, povmlabel)

else: raise ValueError("Invalid spam_metric: %s" % spam_metric)

return ret
assert target_model is not None
"""
Leakage-aware metric supported, per implementation in mdl.frobeniusdist.
Refer to how mdl.frobeniusdist handles the case when transform_mx_arg
is a tuple in order to understand how the leakage-aware metric is defined.
Idea: raise an error if mxBasis isn't leakage-friendly.
PROBLEM: if we're deep in the code then we end up needing to be
working in a basis that has the identity matrix as its
first element. The leakage-friendly basis doesn't even
have the identity matrix as an element, let alone the first element
TODO: dig into function calls below and see where we can
access the mxBasis object. (I'm sure we can.)
Looks like it isn't accessible within ExplicitOpModelCalc objects.
It's most definitely available in ExplicitOpModel.
"""
if "frobenius" in metric:
val = mdl.frobeniusdist(target_model, transform_mx_arg, item_weights)
if "squared" in metric:
val = val ** 2
ret += val
return ret

elif metric == "fidelity":
# Leakage-aware metric supported for gates, using leaky_entanglement_fidelity.
for opLbl in mdl.operations:
wt = item_weights.get(opLbl, opWeight)
top = target_model.operations[opLbl].to_dense()
mop = mdl.operations[opLbl].to_dense()
ret += wt * (1.0 - _tools.leaky_entanglement_fidelity(top, mop, mxBasis, n_leak))**2
# Leakage-aware metrics NOT available for SPAM
for preplabel, m_prep in mdl.preps.items():
wt = item_weights.get(preplabel, spamWeight)
rhoMx1 = _tools.vec_to_stdmx(m_prep.to_dense(), mxBasis)
t_prep = target_model.preps[preplabel]
rhoMx2 = _tools.vec_to_stdmx(t_prep.to_dense(), mxBasis)
ret += wt * (1.0 - _tools.fidelity(rhoMx1, rhoMx2))**2
for povmlabel in mdl.povms.keys():
wt = item_weights.get(povmlabel, spamWeight)
fidelity = _tools.povm_fidelity(mdl, target_model, povmlabel)
ret += wt * (1.0 - fidelity)**2
return ret

elif metric == "tracedist":
# Leakage-aware metric supported, using leaky_jtracedist.
for opLbl in mdl.operations:
wt = item_weights.get(opLbl, opWeight)
top = target_model.operations[opLbl].to_dense()
mop = mdl.operations[opLbl].to_dense()
ret += wt * _tools.leaky_jtracedist(top, mop, mxBasis, n_leak)
# Leakage-aware metrics NOT available for SPAM
for preplabel, m_prep in mdl.preps.items():
wt = item_weights.get(preplabel, spamWeight)
rhoMx1 = _tools.vec_to_stdmx(m_prep.to_dense(), mxBasis)
t_prep = target_model.preps[preplabel]
rhoMx2 = _tools.vec_to_stdmx(t_prep.to_dense(), mxBasis)
ret += wt * _tools.tracedist(rhoMx1, rhoMx2)
for povmlabel in mdl.povms.keys():
wt = item_weights.get(povmlabel, spamWeight)
ret += wt * _tools.povm_jtracedist(mdl, target_model, povmlabel)
return ret

else:
raise ValueError("Invalid metric: %s" % metric)
# end _objective_fn

_jacobian_fn = None

return _objective_fn, _jacobian_fn
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