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Match grape infidelity with manually computed one #51

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Merged
ajgpitch merged 3 commits into from
Aug 15, 2025
Merged

Match grape infidelity with manually computed one #51

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eae7fca
match grape infid with manually computed one
rochisha0 Jul 22, 2025
085a9d8
revert result changes
rochisha0 Jul 23, 2025
311e769
fix objective loop
rochisha0 Aug 7, 2025
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69 changes: 44 additions & 25 deletions src/qutip_qoc/pulse_optim.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,33 +196,52 @@ def optimize_pulses(
"gtol": algorithm_kwargs.get("min_grad", 0.0 if alg == "CRAB" else 1e-8),
}
# Iterate over objectives and convert initial and target states based on the optimization type
for objective in objectives:
H_list = objective.H if isinstance(objective.H, list) else [objective.H]
if any(qt.issuper(H_i) for H_i in H_list):
if isinstance(optimization_type, str) and optimization_type.lower() == "state_transfer":
if qt.isket(objective.initial):
objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
elif qt.isoper(objective.initial):
H_list = objective.H if isinstance(objective.H, list) else [objective.H]
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if any(qt.issuper(H_i) for H_i in H_list):
if isinstance(optimization_type, str) and optimization_type.lower() == "state_transfer":
if qt.isket(objective.initial):
dim = objective.initial.shape[0]
objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
elif qt.isoper(objective.initial):
dim = objective.initial.shape[0]
objective.initial = qt.operator_to_vector(objective.initial)

if qt.isket(objective.target):
objective.target = qt.operator_to_vector(qt.ket2dm(objective.target))
elif qt.isoper(objective.target):
objective.target = qt.operator_to_vector(objective.target)

algorithm_kwargs.setdefault("fid_params", {})
algorithm_kwargs["fid_params"].setdefault("scale_factor", 1.0 / dim)

elif isinstance(optimization_type, str) and optimization_type.lower() == "gate_synthesis":
objective.initial = qt.to_super(objective.initial)
objective.target = qt.to_super(objective.target)

elif optimization_type is None:
is_state_transfer = False
if qt.isoper(objective.initial) and qt.isoper(objective.target):
if np.isclose(objective.initial.tr(), 1) and np.isclose(objective.target.tr(), 1):
dim = objective.initial.shape[0]
objective.initial = qt.operator_to_vector(objective.initial)
if qt.isket(objective.target):
objective.target = qt.operator_to_vector(qt.ket2dm(objective.target))
elif qt.isoper(objective.target):
objective.target = qt.operator_to_vector(objective.target)
elif isinstance(optimization_type, str) and optimization_type.lower() == "gate_synthesis":
objective.initial = qt.to_super(objective.initial)
objective.target = qt.to_super(objective.target)
elif optimization_type is None:
if qt.isoper(objective.initial) and qt.isoper(objective.target):
if np.isclose((objective.initial).tr(), 1) and np.isclose((objective.target).tr(), 1):
objective.initial = qt.operator_to_vector(objective.initial)
objective.target = qt.operator_to_vector(objective.target)
else:
objective.initial = qt.to_super(objective.initial)
objective.target = qt.to_super(objective.target)
if qt.isket(objective.initial):
objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
if qt.isket(objective.target):
objective.target = qt.operator_to_vector(qt.ket2dm(objective.target))
is_state_transfer = True
else:
objective.initial = qt.to_super(objective.initial)
objective.target = qt.to_super(objective.target)

if qt.isket(objective.initial):
dim = objective.initial.shape[0]
objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
is_state_transfer = True

if qt.isket(objective.target):
objective.target = qt.operator_to_vector(qt.ket2dm(objective.target))
is_state_transfer = True

if is_state_transfer:
algorithm_kwargs.setdefault("fid_params", {})
algorithm_kwargs["fid_params"].setdefault("scale_factor", 1.0 / dim)

# prepare qtrl optimizers
qtrl_optimizers = []
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