gpt.qiskit.error.detection.py

import qiskit
from qiskit import QuantumCircuit, transpile, assemble, Aer
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
import numpy as np

# Step 1: Data Generation
def generate_quantum_data(n_qubits, n_circuits):
    # Use Qiskit to generate quantum data
    circuits = []
    for _ in range(n_circuits):
        circuit = QuantumCircuit(n_qubits, n_qubits)
        circuit.h(range(n_qubits))
        circuit.measure(range(n_qubits), range(n_qubits))
        circuits.append(circuit)
    return circuits

# Step 2: Data Preprocessing
def preprocess_data(circuits):
    # Convert quantum data to a format suitable for GPT
    simulator = Aer.get_backend('qasm_simulator')
    data = []
    for circuit in circuits:
        job = assemble(circuit)
        result = simulator.run(job).result()
        counts = result.get_counts(circuit)
        data.append(counts)
    return data

# Step 3: Model Training
def train_model(data):
    # Load preprocessed data
    # Initialize GPT model and tokenizer
    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    model = GPT2LMHeadModel.from_pretrained('gpt2')

    # Define training loop
    num_epochs = 10
    optimizer = torch.optim.Adam(model.parameters())
    for epoch in range(num_epochs):
        for batch in data:
            # Forward pass
            inputs = tokenizer(batch, return_tensors='pt')
            outputs = model(**inputs, labels=inputs["input_ids"])
            loss = outputs.loss

            # Backward pass and optimization
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

# Step 4: Error Detection
def detect_errors(model, tokenizer, data):
    # Use trained model to detect errors in new quantum computations
    errors = []
    for batch in data:
        inputs = tokenizer(batch, return_tensors='pt')
        outputs = model(**inputs)
        predicted_ids = torch.argmax(outputs.logits, dim=-1)
        predicted_batch = tokenizer.decode(predicted_ids[0])
        if predicted_batch != batch:
            errors.append((batch, predicted_batch))
    return errors

# Step 5: Logging
def log_results(errors):
    # Log the results of error detection
    for error in errors:
        print(f"Original: {error[0]}, Predicted: {error[1]}")

# Main function
def main():
    n_qubits = 2
    n_circuits = 100
    circuits = generate_quantum_data(n_qubits, n_circuits)
    data = preprocess_data(circuits)
    train_model(data)
    errors = detect_errors(model, tokenizer, data)
    log_results(errors)

if __name__ == "__main__":
    main()