Summary Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
Cirq - Quantum Computing with Python
Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.
When to Use This Skill
Use this skill when:
Building, simulating, or optimizing NISQ circuits in Python
Running jobs on Google Quantum AI processors (via cirq-google) or partner backends (IonQ, Azure Quantum, AQT, Pasqal)
Modeling noise, compiling to hardware gatesets, or designing characterization experiments
Using parameter sweeps, transformers, or the ReCirq experiment patterns
For IBM hardware use qiskit ; for quantum ML with autodiff use pennylane ; for physics simulations use qutip .
Installation
Requires Python 3.11+. Current stable release: 1.6.1 (August 2025). Vendor packages share the same version number.
uv pip install "cirq==1.6.1"
For hardware integration (pin matching versions for reproducibility):
# Google Quantum Engine (requires approved GCP project access)
uv pip install "cirq-google==1.6.1"
# IonQ
uv pip install "cirq-ionq==1.6.1"
# AQT (Alpine Quantum Technologies)
uv pip install "cirq-aqt==1.6.1"
# Pasqal
uv pip install "cirq-pasqal==1.6.1"
# Azure Quantum (IonQ, Honeywell/Quantinuum backends)
uv pip install "azure-quantum[cirq]"
For latest features during development, omit version pins; for production or hardware runs, pin all packages to the same Cirq release.
Quick Start
Basic Circuit
import cirq
import numpy as np
# Create qubits
q0, q1 = cirq.LineQubit.range(2)
# Build circuit
circuit = cirq.Circuit(
cirq.H(q0), # Hadamard on q0
cirq.CNOT(q0, q1), # CNOT with q0 control, q1 target
cirq.measure(q0, q1, key='result')
)
print(circuit)
# Simulate
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1000)
# Display results
print(result.histogram(key='result'))
Parameterized Circuit import sympy
# Define symbolic parameter
theta = sympy.Symbol('theta')
# Create parameterized circuit
circuit = cirq.Circuit(
cirq.ry(theta)(q0),
cirq.measure(q0, key='m')
)
# Sweep over parameter values
sweep = cirq.Linspace('theta', start=0, stop=2*np.pi, length=20)
results = simulator.run_sweep(circuit, params=sweep, repetitions=1000)
# Process results
for params, result in zip(sweep, results):
theta_val = params['theta']
counts = result.histogram(key='m')
print(f"θ={theta_val:.2f}: {counts}")
Core Capabilities
Circuit Building For comprehensive information about building quantum circuits, including qubits, gates, operations, custom gates, and circuit patterns, see:
Qubit types (GridQubit, LineQubit, NamedQubit)
Single and two-qubit gates
Parameterized gates and operations
Custom gate decomposition
Circuit organization with moments
Standard circuit patterns (Bell states, GHZ, QFT)
Import/export (OpenQASM, JSON)
Working with qudits and observables
Simulation For detailed information about simulating quantum circuits, including exact simulation, noisy simulation, parameter sweeps, and the Quantum Virtual Machine, see:
Exact simulation (state vector, density matrix)
Sampling and measurements
Parameter sweeps (single and multiple parameters)
Noisy simulation
State histograms and visualization
Quantum Virtual Machine (QVM)
Expectation values and observables
Performance optimization
Circuit Transformation For information about optimizing, compiling, and manipulating quantum circuits, see:
Transformer framework
Gate decomposition
Circuit optimization (merge gates, eject Z gates, drop negligible operations)
Circuit compilation for hardware
Qubit routing and SWAP insertion
Custom transformers
Transformation pipelines
Hardware Integration For information about running circuits on real quantum hardware from various providers, see:
Google Quantum AI (cirq-google) — Sycamore, Weber, Willow processors via Quantum Engine (restricted access; requires approved GCP project)IonQ (cirq-ionq) — trapped-ion QPUs and simulatorsAzure Quantum (azure-quantum[cirq]) — IonQ and Honeywell/Quantinuum backendsAQT (cirq-aqt) — Alpine Quantum TechnologiesPasqal (cirq-pasqal) — neutral-atom devices Topics include device representation, qubit selection, authentication, job management, and circuit optimization for hardware. See Access and authentication for Google Cloud setup.
Noise Modeling For information about modeling noise, noisy simulation, characterization, and error mitigation, see:
Noise channels (depolarizing, amplitude damping, phase damping)
Noise models (constant, gate-specific, qubit-specific, thermal)
Adding noise to circuits
Readout noise
Noise characterization (randomized benchmarking, XEB)
Noise visualization (heatmaps)
Error mitigation techniques
Quantum Experiments For information about designing experiments, parameter sweeps, data collection, and using the ReCirq framework, see:
Experiment design patterns
Parameter sweeps and data collection
ReCirq framework structure
Common algorithms (VQE, QAOA, QPE)
Data analysis and visualization
Statistical analysis and fidelity estimation
Parallel data collection
Common Patterns
Variational Algorithm Template import scipy.optimize
def variational_algorithm(ansatz, cost_function, initial_params):
"""Template for variational quantum algorithms."""
def objective(params):
circuit = ansatz(params)
simulator = cirq.Simulator()
result = simulator.simulate(circuit)
return cost_function(result)
# Optimize
result = scipy.optimize.minimize(
objective,
initial_params,
method='COBYLA'
)
return result
# Define ansatz
def my_ansatz(params):
q = cirq.LineQubit(0)
return cirq.Circuit(
cirq.ry(params[0])(q),
cirq.rz(params[1])(q)
)
# Define cost function
def my_cost(result):
state = result.final_state_vector
# Calculate cost based on state
return np.real(state[0])
# Run optimization
result = variational_algorithm(my_ansatz, my_cost, [0.0, 0.0])
Hardware Execution Template import os
def run_on_hardware(circuit, provider='google', processor_id=None, repetitions=1000):
"""Template for running on quantum hardware."""
if provider == 'google':
import cirq_google as cg
project_id = os.environ['GOOGLE_CLOUD_PROJECT']
engine = cg.Engine(project_id=project_id)
# List available processors: engine.list_processors()
processor_id = processor_id or 'weber' # use your assigned processor_id
sampler = engine.get_sampler(processor_id=processor_id)
return sampler.run(circuit, repetitions=repetitions)
elif provider == 'ionq':
import cirq_ionq as ionq
# Requires IONQ_API_KEY in environment
service = ionq.Service()
return service.run(circuit, repetitions=repetitions, target='qpu')
elif provider == 'azure':
from azure.quantum.cirq import AzureQuantumService
service = AzureQuantumService(
resource_id=os.environ['AZURE_QUANTUM_RESOURCE_ID'],
location=os.environ['AZURE_QUANTUM_LOCATION'],
)
return service.run(circuit, repetitions=repetitions, target='ionq.qpu')
else:
raise ValueError(f"Unknown provider: {provider}")
Noise Study Template def noise_comparison_study(circuit, noise_levels):
"""Compare circuit performance at different noise levels."""
results = {}
for noise_level in noise_levels:
# Create noisy circuit
noisy_circuit = circuit.with_noise(cirq.depolarize(p=noise_level))
# Simulate
simulator = cirq.DensityMatrixSimulator()
result = simulator.run(noisy_circuit, repetitions=1000)
# Analyze
results[noise_level] = {
'histogram': result.histogram(key='result'),
'dominant_state': max(
result.histogram(key='result').items(),
key=lambda x: x[1]
)
}
return results
# Run study
noise_levels = [0.0, 0.001, 0.01, 0.05, 0.1]
results = noise_comparison_study(circuit, noise_levels)
Best Practices
Circuit Design
Use appropriate qubit types for your topology
Keep circuits modular and reusable
Label measurements with descriptive keys
Validate circuits against device constraints before execution
Simulation
Use state vector simulation for pure states (more efficient)
Use density matrix simulation only when needed (mixed states, noise)
Leverage parameter sweeps instead of individual runs
Monitor memory usage for large systems (2^n grows quickly)
Hardware Execution
Always test on simulators first
Select best qubits using calibration data
Optimize circuits for target hardware gateset
Implement error mitigation for production runs
Store expensive hardware results immediately
Circuit Optimization
Start with high-level built-in transformers
Chain multiple optimizations in sequence
Track depth and gate count reduction
Validate correctness after transformation
Noise Modeling
Use realistic noise models from calibration data
Include all error sources (gate, decoherence, readout)
Characterize before mitigating
Keep circuits shallow to minimize noise accumulation
Experiments
Structure experiments with clear separation (data generation, collection, analysis)
Use ReCirq patterns for reproducibility
Save intermediate results frequently
Parallelize independent tasks
Document thoroughly with metadata
Additional Resources
Common Issues Circuit too deep for hardware:
Use circuit optimization transformers to reduce depth
See transformation.md for optimization techniques
Memory issues with simulation:
Switch from density matrix to state vector simulator
Reduce number of qubits or use stabilizer simulator for Clifford circuits
Device validation errors:
Check qubit connectivity with device.metadata.nx_graph
Decompose gates to device-native gateset
See hardware.md for device-specific compilation
Noisy simulation too slow:
Density matrix simulation is O(2^2n) - consider reducing qubits
Use noise models selectively on critical operations only
See simulation.md for performance optimization
