Google Cirq

pip install cirq           # Core + simulator
pip install cirq-google    # Google hardware access (optional)
pip install cirq-web       # Web visualization (optional)

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⟩)

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)

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'))

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}")