
About
Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.
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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-googleandcirq-ionq. - You are prototyping or teaching quantum workflows in Python and want concrete circuit examples.
Installation
uv pip install cirq
For hardware integration:
# Google Quantum Engine
uv pip install cirq-google
# IonQ
uv pip install cirq-ionq
# AQT (Alpine Quantum Technologies)
uv pip install cirq-aqt
# Pasqal
uv pip install cirq-pasqal
# Azure Quantum
uv pip install azure-quantum cirq
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:
- references/building.md - Complete guide to circuit construction
Common topics:
- 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
Common topics:
- 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
Common topics:
- 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
Supported providers:
- Google Quantum AI (cirq-google) - Sycamore, Weber processors
- IonQ (cirq-ionq) - Trapped ion quantum computers
- Azure Quantum (azure-quantum) - IonQ and Honeywell backends
- AQT (cirq-aqt) - Alpine Quantum Technologies
- Pasqal (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
Common topics:
- 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,
