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name cirq description Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators. type skill created 2026-02-27T00:00:00.000Z domain productivity category developer-experience risk unknown source community tags ["skill","productivity","developer-experience","cirq"]
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
You are designing, simulating, or executing quantum circuits with the Cirq ecosystem.
You need Google Quantum AI-style primitives, parameterized circuits, or integrations like cirq-google and cirq-ionq.
You are prototyping or teaching quantum workflows in Python and want concrete circuit examples.
Installation
uv pip install cirq
For hardware integration:
uv pip install cirq-google
uv pip install cirq-ionq
uv pip install cirq-aqt
uv pip install cirq-pasqal
uv pip install azure-quantum cirq
Quick Start
Basic Circuit
import cirq
import numpy as np
q0, q1 = cirq.LineQubit.range (2 )
circuit = cirq.Circuit(
cirq.H(q0),
cirq.CNOT(q0, q1),
cirq.measure(q0, q1, key='result' )
)
print (circuit)
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1000 )
print (result.histogram(key='result' ))
Parameterized Circuit
import sympy
theta = sympy.Symbol('theta' )
circuit = cirq.Circuit(
cirq.ry(theta)(q0),
cirq.measure(q0, key='m'
'theta'
0
2
20
1000
for
in
zip
'theta'
'm'
print
f"θ={theta_val:.2 f} : {counts} "
Core Capabilities
Circuit Building For comprehensive information about building quantum circuits, including qubits, gates, operations, custom gates, and circuit patterns, see:
references/building.md - Complete guide to circuit construction
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:
references/simulation.md - Complete guide to quantum simulation
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:
references/transformation.md - Complete guide to circuit transformations
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:
references/hardware.md - Complete guide to hardware integration
Google Quantum AI (cirq-google) - Sycamore, Weber processorsIonQ (cirq-ionq) - Trapped ion quantum computersAzure Quantum (azure-quantum) - IonQ and Honeywell backendsAQT (cirq-aqt) - Alpine Quantum TechnologiesPasqal (cirq-pasqal) - Neutral atom quantum computers Topics include device representation, qubit selection, authentication, job management, and circuit optimization for hardware.
Noise Modeling For information about modeling noise, noisy simulation, characterization, and error mitigation, see:
references/noise.md - Complete guide to noise modeling
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:
references/experiments.md - Complete guide to quantum experiments
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)
result = scipy.optimize.minimize(
objective,
initial_params,
method='COBYLA'
)
return result
def my_ansatz (params ):
q = cirq.LineQubit(0 )
return cirq.Circuit(
cirq.ry(params[0 ])(q),
cirq.rz(params[1 ])(q)
)
def my_cost (result ):
state = result.final_state_vector
return np.real(state[0 ])
result = variational_algorithm(my_ansatz, my_cost, [0.0 , 0.0 ])
Hardware Execution Template def run_on_hardware (circuit, provider='google' , device_name='weber' , repetitions=1000 ):
"""Template for running on quantum hardware."""
if provider == 'google' :
import cirq_google
engine = cirq_google.get_engine()
processor = engine.get_processor(device_name)
job = processor.run(circuit, repetitions=repetitions)
return job.results()[0 ]
elif provider == 'ionq' :
import cirq_ionq
service = cirq_ionq.Service()
result = service.run(circuit, repetitions=repetitions, target='qpu' )
return result
elif provider == 'azure' :
from azure.quantum.cirq import AzureQuantumService
service = AzureQuantumService(workspace)
result = service.run(circuit, repetitions=repetitions, target='ionq.qpu' )
return result
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:
noisy_circuit = circuit.with_noise(cirq.depolarize(p=noise_level))
simulator = cirq.DensityMatrixSimulator()
result = simulator.run(noisy_circuit, repetitions=1000 )
results[noise_level] = {
'histogram' : result.histogram(key='result' ),
'dominant_state' : max (
result.histogram(key='result' ).items(),
key=lambda x: x[1 ]
)
}
return results
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
Connections
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