QCIL PROTOTYPE
# === QCIL Prototype: Adaptive Quantum Control Loop ===
from qiskit import QuantumCircuit, Aer, execute
from qiskit.providers.aer.noise import NoiseModel, depolarizing_error
import numpy as np
# =========================
# QCIL Core Modules
# =========================
class EntropyEstimator:
def estimate(self, data_window):
# Stability proxy: RMS variation
return np.std(data_window)
class BiasMemory:
def __init__(self):
self.history = []
def update(self, value):
self.history.append(value)
def get_bias_adjustment(self, window=5):
if len(self.history) < window:
return 0
return np.mean(self.history[-window:])
class DriftGovernor:
def clamp(self, update, limit=0.001):
return np.clip(update, -limit, limit)
class PulseScheduler:
def schedule(self, circuit, adjust):
# Symbolic control injection (gate tuning proxy)
if abs(adjust) > 0:
circuit.ry(adjust, 0)
return circuit
# =========================
# Quantum Circuit Base
# =========================
qc_base = QuantumCircuit(2, 2)
qc_base.h(0)
qc_base.cx(0, 1)
qc_base.measure([0, 1], [0, 1])
backend = Aer.get_backend('qasm_simulator')
# =========================
# QCIL Module Initialization
# =========================
entropy_gate = EntropyEstimator()
bias_mem = BiasMemory()
drift_gov = DriftGovernor()
scheduler = PulseScheduler()
# =========================
# Simulation Buffers
# =========================
fidelity_buffer = []
entropy_log = []
fidelity_log = []
# =========================
# QCIL Control Loop
# =========================
for t in range(50):
# Inject time-varying symbolic drift (noise growth)
noise_model = NoiseModel()
drift_strength = 0.01 + (t * 0.001)
error = depolarizing_error(drift_strength, 2)
noise_model.add_all_qubit_quantum_error(error, ['cx'])
# Copy base circuit
qc = qc_base.copy()
# Apply symbolic bias correction
bias_adjust = bias_mem.get_bias_adjustment()
adjusted_qc = scheduler.schedule(qc, bias_adjust)
# Execute simulation
job = execute(adjusted_qc,
backend=backend,
noise_model=noise_model,
shots=1024)
result = job.result()
counts = result.get_counts()
# Target Bell state fidelity proxy
fidelity = counts.get('00', 0) / 1024
fidelity_buffer.append(fidelity)
fidelity_log.append(fidelity)
# Windowed entropy estimate
entropy = entropy_gate.estimate(fidelity_buffer[-10:])
entropy_log.append(entropy)
# QCIL Gate Decision
if entropy < 0.01:
bias_mem.update(1.0 - fidelity)
delta = drift_gov.clamp(1.0 - fidelity)
print(f"[QCIL] Step {t} | Pulse Allowed | Fidelity={fidelity:.4f} | ΔΩ={delta:.5f}")
else:
print(f"[QCIL] Step {t} | Execution Paused | Entropy={entropy:.5f}")
print("\nQCIL Simulation Complete.")
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