quantum_compression.py

from qiskit import QuantumCircuit, Aer, transpile, assemble, execute
from qiskit.circuit.library import EfficientSU2
from qiskit.algorithms.optimizers import COBYLA
from qiskit.algorithms import VQE
from qiskit.utils import QuantumInstance
from qiskit.providers.aer import AerSimulator
from qiskit.visualization import plot_histogram

# Function to create a variational quantum circuit
def create_variational_circuit(data):
    num_qubits = len(data)
    feature_map = EfficientSU2(num_qubits, reps=1)
    qc = QuantumCircuit(num_qubits)
    qc.compose(feature_map, inplace=True)
    return qc

# Function to perform variational quantum compression
def variational_quantum_compression(data):
    num_qubits = len(data)
    qc = create_variational_circuit(data)
    optimizer = COBYLA(maxiter=100)
    vqe = VQE(qc, optimizer=optimizer, quantum_instance=QuantumInstance(Aer.get_backend('statevector_simulator')))
    result = vqe.compute_minimum_eigenvalue()
    return result.optimal_point

# Function to simulate quantum circuit with noise mitigation
def simulate_quantum_circuit_with_noise(qc):
    simulator = AerSimulator()
    t_qc = transpile(qc, simulator)
    job = execute(t_qc, backend=simulator, shots=1024)
    result = job.result()
    counts = result.get_counts()
    return counts

# Function to create a quantum autoencoder
def create_quantum_autoencoder(data):
    num_qubits = len(data)
    encoder = QuantumCircuit(num_qubits)
    for i in range(num_qubits):
        encoder.h(i)
        encoder.cx(i, (i + 1) % num_qubits)
    decoder = encoder.inverse()
    return encoder, decoder

# Function to perform quantum autoencoder compression
def quantum_autoencoder_compression(data):
    encoder, decoder = create_quantum_autoencoder(data)
    qc = QuantumCircuit(len(data) * 2)
    qc.compose(encoder, inplace=True)
    qc.compose(decoder, inplace=True)
    return qc

# Visualization function for quantum results
def visualize_quantum_results(counts):
    plot_histogram(counts).show()