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fix parameter initialization #359

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Merged
MoritzWillmann merged 3 commits into from
Nov 14, 2025
Merged

fix parameter initialization #359

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a82c15a
fix parameter initialization
MoritzWillmann Nov 6, 2025
6479139
return if fitted
MoritzWillmann Nov 6, 2025
708e11a
Merge branch 'develop' into fix_param_initialization
MoritzWillmann Nov 14, 2025
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59 changes: 29 additions & 30 deletions src/squlearn/qnn/base_qnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,37 +193,7 @@ def fit(self, X, y, weights: np.ndarray = None) -> None:
Labels
weights: Weights for each data point
"""
self.encoding_circuit._check_feature_consistency(X)
num_features = extract_num_features(X)

self._is_lowlevel_qnn_initialized = False
self._initialize_lowlevel_qnn(num_features)

if self.param_ini is None or len(self.param_ini) != self._qnn.num_parameters:
self._param = self.encoding_circuit.generate_initial_parameters(
seed=self.parameter_seed, num_features=num_features
)
else:
self._param = self.param_ini.copy()

if (
self.param_op_ini is None
or len(self.param_op_ini) != self._qnn.num_parameters_observable
):
if isinstance(self.operator, list):
self._param_op = np.concatenate(
[
operator.generate_initial_parameters(seed=self.parameter_seed + i + 1)
for i, operator in enumerate(self.operator)
]
)
else:
self._param_op = self.operator.generate_initial_parameters(
seed=self.parameter_seed + 1
)
else:
self._param_op = self.param_op_ini.copy()

self._is_fitted = False
self._fit(X, y, weights)

Expand Down Expand Up @@ -387,6 +357,35 @@ def _initialize_lowlevel_qnn(self, num_features: int | None = None) -> None:
caching=self.caching,
primitive=self.primitive,
)

if self._is_fitted:
self._is_lowlevel_qnn_initialized = True
return

if self.param_ini is None or len(self.param_ini) != self.encoding_circuit.num_parameters:
self._param = self.encoding_circuit.generate_initial_parameters(
seed=self.parameter_seed, num_features=num_features
)
else:
self._param = self.param_ini.copy()

if (
self.param_op_ini is None
or len(self.param_op_ini) != self._qnn.num_parameters_observable
):
if isinstance(self.operator, list):
self._param_op = np.concatenate(
[
operator.generate_initial_parameters(seed=self.parameter_seed + i + 1)
for i, operator in enumerate(self.operator)
]
)
else:
self._param_op = self.operator.generate_initial_parameters(
seed=self.parameter_seed + 1
)
else:
self._param_op = self.param_op_ini.copy()
self._is_lowlevel_qnn_initialized = True

def _validate_input(self, X, y, incremental, reset):
Expand Down