Qu'est-ce que Cirq ?
Cirq est le framework d'informatique quantique de Google conçu pour les appareils NISQ (Noisy Intermediate-Scale Quantum). Il offre un contrôle fin des opérations au niveau des portes, ce qui le rend idéal pour le développement d'algorithmes et les expériences matérielles. Cirq inclut plusieurs simulateurs gratuits et peut s'exécuter sur le matériel quantique de Google via des partenariats.
Installation
terminal
pip install cirq # Core + simulator pip install cirq-google # Google hardware access (optional) pip install cirq-web # Web visualization (optional)Circuit et simulation de base
cirq_basic.py
import cirq import numpy as np # Create qubits q0, q1 = cirq.LineQubit.range(2) # Build a Bell state circuit circuit = cirq.Circuit([ cirq.H(q0), cirq.CNOT(q0, q1), cirq.measure(q0, q1, key='result') ]) print(circuit) # 0: ───H───@───M('result')─── # │ │ # 1: ───────X───M───────────── # Simulate with shots (sampling) sim = cirq.Simulator() result = sim.run(circuit, repetitions=1000) print(result.histogram(key='result')) # Counter({0: 504, 3: 496}) (0=|00⟩, 3=|11⟩)Simulation par vecteur d'état et matrice de densité
cirq_statevector.py
import cirq q0, q1 = cirq.LineQubit.range(2) # Build without measurement for statevector circuit = cirq.Circuit([cirq.H(q0), cirq.CNOT(q0, q1)]) # Exact statevector simulation (free, local) sim = cirq.Simulator() result = sim.simulate(circuit) print(result.final_state_vector) # [0.707+0j, 0+0j, 0+0j, 0.707+0j] # Density matrix simulation (for noisy circuits) noise_model = cirq.ConstantQubitNoiseModel( cirq.depolarize(p=0.01) ) noisy_sim = cirq.DensityMatrixSimulator(noise=noise_model) noisy_result = noisy_sim.simulate(circuit) print(noisy_result.final_density_matrix)Simulateur Clifford (efficace pour les circuits stabilisateurs)
cirq_clifford.py
import cirq # CliffordSimulator efficiently handles stabilizer circuits # Simulates 1000s of qubits for Clifford gates qubits = cirq.LineQubit.range(50) # 50 qubits! circuit = cirq.Circuit( [cirq.H(q) for q in qubits], [cirq.CNOT(qubits[i], qubits[i+1]) for i in range(49)], cirq.measure(*qubits, key='ghz') ) sim = cirq.CliffordSimulator() result = sim.run(circuit, repetitions=100) print(result.histogram(key='ghz'))Algorithmes variationnels avec Cirq
cirq_vqa.py
import cirq import numpy as np from scipy.optimize import minimize q0, q1 = cirq.LineQubit.range(2) def ansatz(theta: float) -> cirq.Circuit: return cirq.Circuit([ cirq.ry(theta)(q0), cirq.CNOT(q0, q1), cirq.measure(q0, q1, key='m') ]) def cost(params): circuit = ansatz(params[0]) sim = cirq.Simulator() result = sim.run(circuit, repetitions=200) counts = result.histogram(key='m') # Minimize energy (simplified objective) return -counts.get(0, 0) / 200 # maximize |00⟩ prob result = minimize(cost, x0=[0.5], method='COBYLA') print(f"Optimal theta: {result.x[0]:.4f}")💡
Also available via HLQuantum
Want to run the same circuit on multiple backends without rewriting your code? HLQuantum abstracts this SDK (and 5 others) behind a single unified API.
python
import hlquantum as hlq qc = hlq.Circuit(2) qc.h(0).cx(0, 1).measure_all() # One line to switch between any backend result = hlq.run(qc, shots=1024) # auto-detect result = hlq.run(qc, shots=1024, backend="cirq") # explicit