From e787390afed2f39282205270ee80cc36dae66c24 Mon Sep 17 00:00:00 2001 From: KOSASIH Date: Sat, 13 Jul 2024 16:41:29 +0700 Subject: [PATCH] Create neural_network_quantum.py --- ai_engine/neural_network_quantum.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 ai_engine/neural_network_quantum.py diff --git a/ai_engine/neural_network_quantum.py b/ai_engine/neural_network_quantum.py new file mode 100644 index 0000000..5fd5e85 --- /dev/null +++ b/ai_engine/neural_network_quantum.py @@ -0,0 +1,21 @@ +import tensorflow as tf +from qiskit import QuantumCircuit, execute + +class QuantumNeuralNetwork: + def __init__(self, input_shape, num_classes): + self.model = tf.keras.Sequential([ + tf.keras.layers.Dense(64, activation='relu', input_shape=input_shape), + tf.keras.layers.Dense(32, activation='relu'), + tf.keras.layers.Dense(num_classes, activation='softmax') + ]) + self.quantum_circuit = QuantumCircuit(5, 5) + + def train(self, X_train, y_train, epochs=10, batch_size=32): + self.model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.2) + self.quantum_circuit.barrier() + job = execute(self.quantum_circuit, backend='qasm_simulator', shots=1024) + result = job.result() + self.model.set_weights(result.get_statevector()) + + def predict(self, X_test): + return self.model.predict(X_test)