Create quantum_reinforcement_learning.py

evolution/machine_learning/quantum_reinforcement_learning.py

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+# quantum_reinforcement_learning.py
+import numpy as np
+from qiskit import QuantumCircuit, execute
+from qiskit.providers.aer import AerSimulator
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Dense, LSTM
+
+class QuantumReinforcementLearning:
+    def __init__(self, num_qubits, num_layers):
+        self.num_qubits = num_qubits
+        self.num_layers = num_layers
+        self.quantum_backend = AerSimulator()
+        self.quantum_circuit = QuantumCircuit(num_qubits)
+
+    def _generate_quantum_circuit(self):
+        for i in range(self.num_layers):
+            self.quantum_circuit.ry(np.pi / 2, i)
+            self.quantum_circuit.rz(np.pi / 2, i)
+        return self.quantum_circuit
+
+    def _train_quantum_model(self, X, y):
+        quantum_circuit = self._generate_quantum_circuit()
+        job = execute(quantum_circuit, self.quantum_backend, shots=1024)
+        result = job.result()
+        counts = result.get_counts(quantum_circuit)
+        return counts
+
+    def _train_classical_model(self, X, y):
+        model = Model(inputs=Input(shape=(X.shape[1],)), outputs=Dense(y.shape[1], activation='softmax')(LSTM(64)(Input(shape=(X.shape[1],)))))
+        model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+        model.fit(X, y, epochs=10, batch_size=32, validation_data=(X, y))
+        return model
+
+    def train(self, X, y):
+        quantum_counts = self._train_quantum_model(X, y)
+        classical_model = self._train_classical_model(X, y)
+        return quantum_counts, classical_model
+
+# Example usage:
+X = np.random.rand(100, 10)
+y = np.random.rand(100, 10)
+qrl = QuantumReinforcementLearning(num_qubits=10, num_layers=5)
+quantum_counts, classical_model = qrl.train(X, y)