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Fix state transfer not working for GRAPE and CRAB #36

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Merged
merged 5 commits into from
Mar 28, 2025
Merged

Fix state transfer not working for GRAPE and CRAB #36

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b0fd00c
add check for operator input
rochisha0 Feb 26, 2025
37de33f
Fix objective for open system
rochisha0 Mar 10, 2025
e2781bc
add logic for gate synthesis
rochisha0 Mar 12, 2025
02d361a
improve the clarity of the code
rochisha0 Mar 18, 2025
ae447b6
fix trace
rochisha0 Mar 27, 2025
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6 changes: 0 additions & 6 deletions src/qutip_qoc/objective.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,12 +80,6 @@ def __init__(self, initial, H, target, weight=1):
self.target = target
self.weight = weight

# Check if any Hamiltonian in H is a superoperator
if any(qt.issuper(H_i) for H_i in (H if isinstance(H, list) else [H])):
# Convert initial and target accordingly
self.initial = qt.to_super(self.initial)
self.target = qt.to_super(self.target)

def __getstate__(self):
"""
Extract picklable information from the objective.
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38 changes: 37 additions & 1 deletion src/qutip_qoc/pulse_optim.py
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Expand Up @@ -11,6 +11,8 @@
from qutip_qoc._optimizer import _global_local_optimization
from qutip_qoc._time import _TimeInterval

import qutip as qt

try:
from qutip_qoc._rl import _RL
_rl_available = True
Expand All @@ -28,6 +30,7 @@ def optimize_pulses(
optimizer_kwargs=None,
minimizer_kwargs=None,
integrator_kwargs=None,
optimization_type=None,
):
"""
Run GOAT, JOPT, GRAPE, CRAB or RL optimization.
Expand Down Expand Up @@ -124,6 +127,9 @@ def optimize_pulses(
Options for the solver, see :obj:`MESolver.options` and
`Integrator <./classes.html#classes-ode>`_ for a list of all options.

optimization_type : str, optional
Type of optimization: "state_transfer", "gate_synthesis", or None.

Returns
-------
result : :class:`qutip_qoc.Result`
Expand Down Expand Up @@ -187,10 +193,40 @@ def optimize_pulses(
"maxiter": algorithm_kwargs.get("max_iter", 1000),
"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:
if any(qt.issuper(H_i) for H_i in (objective.H if isinstance(objective.H, list) else [objective.H])):
if optimization_type == "state_transfer":
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To make everything a bit easier to read and maintain, could we refactor a little bit so that this whole block of code becomes, for example

for objective in objectives:
    if objective._is_open():
        objective._format_for_open(optimization_type)

Without changing any of the logic; the logic seems good to me :)

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I have refactored in a different way. Can you please check that, or explain this way more?

if qt.isket(objective.initial):
objective.initial = qt.operator_to_vector(qt.ket2dm(objective.initial))
elif qt.isoper(objective.initial):
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 optimization_type == "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(qt.tr(objective.initial), 1) and np.isclose(qt.tr(objective.target), 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))

# prepare qtrl optimizers
qtrl_optimizers = []
if alg == "CRAB" or alg == "GRAPE":
dyn_type = "GEN_MAT"
for objective in objectives:
if any(qt.isoper(H_i) for H_i in (objective.H if isinstance(objective.H, list) else [objective.H])):
dyn_type = "UNIT"

if alg == "GRAPE": # algorithm specific kwargs
use_as_amps = True
minimizer_kwargs.setdefault("method", "L-BFGS-B") # gradient
Expand Down Expand Up @@ -247,7 +283,7 @@ def optimize_pulses(
"accuracy_factor": None, # deprecated
"alg_params": alg_params,
"optim_params": algorithm_kwargs.get("optim_params", None),
"dyn_type": algorithm_kwargs.get("dyn_type", "GEN_MAT"),
"dyn_type": algorithm_kwargs.get("dyn_type", dyn_type),
"dyn_params": algorithm_kwargs.get("dyn_params", None),
"prop_type": algorithm_kwargs.get(
"prop_type", "DEF"
Expand Down