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qop.py
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qop.py
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from qiskit import QuantumCircuit, Aer, execute
from qiskit.visualization import plot_histogram
from qiskit.quantum_info import Statevector
import numpy as np
import opuslib
class QuantumFilter:
def __init__(self, num_qubits):
self.num_qubits = num_qubits
self.circuit = QuantumCircuit(num_qubits)
def grovers_algorithm(self, oracle):
# Implement Grover's algorithm to find the desired state
self.circuit.h(range(self.num_qubits))
self.circuit.barrier()
iterations = int(np.pi / 4 * np.sqrt(2 ** self.num_qubits))
for _ in range(iterations):
oracle(self.circuit)
self.diffusion_operator()
self.circuit.barrier()
def diffusion_operator(self):
self.circuit.h(range(self.num_qubits))
self.circuit.x(range(self.num_qubits))
self.circuit.h(self.num_qubits - 1)
self.circuit.mct(list(range(self.num_qubits - 1)), self.num_qubits - 1)
self.circuit.h(self.num_qubits - 1)
self.circuit.x(range(self.num_qubits))
self.circuit.h(range(self.num_qubits))
def shors_algorithm(self, a, N):
# Implement Shor's algorithm to factorize a number
self.circuit.h(range(self.num_qubits))
self.circuit.barrier()
for qubit in range(self.num_qubits):
self.circuit.u1(2 * np.pi * a**(2**qubit) / N, qubit)
self.circuit.barrier()
self.circuit.qft(range(self.num_qubits))
self.circuit.barrier()
def apply_filter(self, input_state, oracle, a, N):
# Apply the quantum filter to the input state
self.grovers_algorithm(oracle)
self.shors_algorithm(a, N)
# Simulate the quantum circuit
backend = Aer.get_backend('statevector_simulator')
result = execute(self.circuit, backend).result()
statevector = result.get_statevector()
# Process the output statevector
filtered_state = self.process_output(input_state, statevector)
return filtered_state
def process_output(self, input_state, statevector):
# Process the output statevector and apply it to the input state
output_state = input_state.copy()
for i in range(len(input_state)):
amplitude = statevector[i]
output_state[i] *= amplitude
return output_state
def oracle(circuit):
# Define the oracle based on the desired state
# Example: Mark the state |1010> as the desired state
circuit.x(1)
circuit.x(3)
circuit.h(2)
circuit.ccx(0, 1, 2)
circuit.h(2)
circuit.x(1)
circuit.x(3)
def process_audio(opus_file, filtered_opus_file, num_qubits, a, N):
# Read audio input using Opus
opus_decoder = opuslib.api.decoder.create_state(48000, 2)
opus_data = open(opus_file, "rb").read()
pcm_data = opuslib.api.decoder.decode(opus_decoder, opus_data, frame_size=960)
opuslib.api.decoder.destroy(opus_decoder)
# Convert PCM data to input state
input_state = pcm_data.flatten()
# Create quantum filter and apply it to the input state
quantum_filter = QuantumFilter(num_qubits)
filtered_state = quantum_filter.apply_filter(input_state, oracle, a, N)
# Convert the filtered state back to PCM data
filtered_pcm_data = np.reshape(filtered_state, (-1, 2))
# Encode the filtered PCM data back to Opus
opus_encoder = opuslib.api.encoder.create_state(48000, 2, opuslib.APPLICATION_AUDIO)
filtered_opus_data = opuslib.api.encoder.encode(opus_encoder, filtered_pcm_data, frame_size=960)
opuslib.api.encoder.destroy(opus_encoder)
# Write the filtered Opus data to a file
with open(filtered_opus_file, "wb") as f:
f.write(filtered_opus_data)
# Example usage
opus_file = "input.opus"
filtered_opus_file = "filtered_output.opus"
num_qubits = 4
a = 7
N = 15
process_audio(opus_file, filtered_opus_file, num_qubits, a, N)
print(f"Quantum filtering completed. Filtered audio saved as '{filtered_opus_file}'.")