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.
Cirq is built for NISQ-era devices — compare it with the other SDK before you commit to one.
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}")Keep exploring
💡
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