use new random_pauli_list (#379)

Author: MoritzWillmann

src/squlearn/qrc/base_qrc.py

@@ -17,8 +17,6 @@ def validate_data(self, *args, **kwargs):
         return self._validate_data(*args, **kwargs)
 
 
-from qiskit.quantum_info.random import random_pauli_list
-
 from ..observables.observable_base import ObservableBase
 from ..observables import CustomObservable, SinglePauli
 from ..encoding_circuit.encoding_circuit_base import EncodingCircuitBase
@@ -27,6 +25,8 @@ def validate_data(self, *args, **kwargs):
 from ..qnn.lowlevel_qnn import LowLevelQNN
 from ..util.serialization import SerializableModelMixin
 
+from .util.random_pauli_list import random_pauli_list
+
 
 class BaseQRC(BaseEstimator, SerializableModelMixin, ABC):
     """Base class for Quantum Reservoir Computing (QRC) models.
@@ -65,7 +65,7 @@ def __init__(
         ml_model: str = "linear",
         ml_model_options: Union[dict, None] = None,
         operators: Union[ObservableBase, list[ObservableBase], str] = "random_paulis",
-        num_operators: int = 100,
+        num_operators: int = None,
         operator_seed: int = 0,
         param_ini: Union[np.ndarray, None] = None,
         param_op_ini: Union[np.ndarray, None] = None,
@@ -89,6 +89,19 @@ def __init__(
         self._is_lowlevel_qnn_initialized = False
         self._qnn = None
 
+        if self.operators == "random_paulis":
+            if (
+                self.num_operators is not None
+                and self.num_operators > 4**self.encoding_circuit.num_qubits
+            ):
+                raise ValueError(
+                    f"Number of operators ({self.num_operators}) exceeds the maximum possible "
+                    f"({4**self.encoding_circuit.num_qubits}) for the given number of qubits "
+                    f"({self.encoding_circuit.num_qubits})."
+                )
+            elif self.num_operators is None:
+                self.num_operators = min(100, 4**self.encoding_circuit.num_qubits)
+
         self._ml_model = None
         self._initialize_observables()
         self._initialize_ml_model()
@@ -152,7 +165,6 @@ def _initialize_observables(self) -> None:
                     self.encoding_circuit.num_qubits,
                     self.num_operators,
                     seed=self.operator_seed,
-                    phase=False,
                 )
                 self._operators = [
                     CustomObservable(self.encoding_circuit.num_qubits, str(p)) for p in paulis

src/squlearn/qrc/qrc_classifier.py

@@ -94,7 +94,7 @@ def __init__(
         ml_model: str = "linear",
         ml_model_options: Union[dict, None] = None,
         operators: Union[ObservableBase, list[ObservableBase], str] = "random_paulis",
-        num_operators: int = 100,
+        num_operators: int = None,
         operator_seed: int = 0,
         param_ini: Union[np.ndarray, None] = None,
         param_op_ini: Union[np.ndarray, None] = None,

src/squlearn/qrc/qrc_regressor.py

@@ -92,7 +92,7 @@ def __init__(
         ml_model: str = "linear",
         ml_model_options: Union[dict, None] = None,
         operators: Union[ObservableBase, list[ObservableBase], str] = "random_paulis",
-        num_operators: int = 100,
+        num_operators: int = None,
         operator_seed: int = 0,
         param_ini: Union[np.ndarray, None] = None,
         param_op_ini: Union[np.ndarray, None] = None,

tests/qrc/test_qrc_classifier.py

@@ -49,7 +49,7 @@ def test_fit_predict(self, data, ml_model):
         values = qrc_classifier.predict(X)
 
         referece_values = {
-            "linear": np.array([0, 1, 0, 1, 1, 1]),
+            "linear": np.array([0, 1, 0, 1, 0, 1]),
             "mlp": np.array([0, 1, 0, 1, 0, 1]),
             "kernel": np.array([0, 1, 0, 0, 0, 1]),
         }

tests/qrc/test_qrc_regressor.py

@@ -53,10 +53,10 @@ def test_fit_predict(self, data, ml_model):
                 [2.88509523, -0.80308901, 3.76200899, -1.35995202, 8.84630756, -1.36004739]
             ),
             "mlp": np.array(
-                [3.06585455, -0.96776248, 4.0395428, -1.52636054, 8.80042369, -1.52645433]
+                [3.04943393, -0.87369101, 3.98814234, -1.42608296, 8.61361362, -1.42617601]
             ),
             "kernel": np.array(
-                [2.87653068, -0.65406699, 3.74209315, -1.13894099, 8.15484098, -1.13902237]
+                [2.54250631, 0.06183984, 3.13005284, -0.29406904, 6.04568976, -0.29412921]
             ),
         }