--- name: cirq description: 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. license: Apache-2.0 license compatibility: Requires Python 3.11+ and Cirq 1.7.0 for local examples. Cloud execution requires provider credentials, network access, and assigned targets. Azure uses a separate Cirq 1.6.1 environment. allowed-tools: Read Write Edit Bash metadata: version: "1.3" last-reviewed: "2026-09-30" skill-author: K-Dense Inc. --- # 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 Examples target **Cirq 1.7.0** on Python 3.11+. Keep Cirq vendor packages on the matching release; independently versioned integrations such as Azure Quantum need their own compatibility check. ```bash uv pip install "cirq-core==1.7.0" # Optional OpenQASM import dependency uv pip install ply ``` For hardware integration (pin matching versions for reproducibility): ```bash # Google Quantum Engine (requires approved GCP project access) uv pip install "cirq-google==1.7.0" # IonQ uv pip install "cirq-ionq==1.7.0" # AQT (Alpine Quantum Technologies) uv pip install "cirq-aqt==1.7.0" # Pasqal uv pip install "cirq-pasqal==1.7.0" # Azure: use a SEPARATE environment; its current extras require Cirq <1.7. uv pip install "qdk[azure,cirq]==1.32.3" "azure-quantum==3.13.0" ``` Pin all `cirq-*` packages in an environment to the same release. The Azure example targets its supported Cirq 1.6.1 stack separately; do not force Cirq 1.7.0 into it. Cirq 1.7.0 updates the IonQ adapter to API v0.4. Local examples were checked with synthetic circuits. Hardware examples are illustrative and were checked against released source and provider documentation, without submitting cloud jobs. See [review evidence](references/review.md). ## Quick Start ### Basic Circuit ```python 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(seed=42) result = simulator.run(circuit, repetitions=1000) # Display results print(result.histogram(key='result')) ``` ### Parameterized Circuit ```python 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](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](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](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](references/hardware.md)** - Complete guide to hardware integration Supported providers: - **Google Quantum AI** (`cirq-google`) — assigned processors via Quantum Engine; discover IDs from your approved project. Bundled virtual processor names are not proof of live access. - **IonQ** (`cirq-ionq`) — trapped-ion QPUs and simulators - **Azure Quantum** (`qdk[azure,cirq]`) — discover Cirq-compatible targets in your workspace, in its separate supported environment - **AQT** (`cirq-aqt`) — Alpine Quantum Technologies - **Pasqal** (`cirq-pasqal`) — local device modeling; the legacy remote sampler is not a verified current Pasqal Cloud integration Topics include device representation, qubit selection, authentication, job management, and circuit optimization for hardware. See [Access and authentication](https://quantumai.google/cirq/google/access) for Google Cloud setup. ### Noise Modeling For information about modeling noise, noisy simulation, characterization, and error mitigation, see: - **[references/noise.md](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, see: - **[references/experiments.md](references/experiments.md)** - Complete guide to quantum experiments Common topics: - 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 ```python 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 # Single-qubit Pauli-Z expectation; invariant to global phase. return float(abs(state[0])**2 - abs(state[1])**2) # Run optimization result = variational_algorithm(my_ansatz, my_cost, [0.0, 0.0]) ``` ### Hardware Execution Template Illustrative: requires provider credentials, assigned processor IDs, and current provider target names. The local simulation examples do not validate QPU access. ```python import cirq def run_on_hardware(circuit, sampler, device, repetitions=1000): """Submit an already routed/compiled circuit to an explicitly chosen sampler.""" device.validate_circuit(circuit) return sampler.run(circuit, repetitions=repetitions) ``` Configure a provider-specific sampler using [hardware.md](references/hardware.md). Select the target explicitly; compilation does not itself route disconnected qubits or guarantee hardware access. ### Noise Study Template ```python 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 on a Bell circuit with the measurement key expected above. q0, q1 = cirq.LineQubit.range(2) circuit = cirq.Circuit(cirq.H(q0), cirq.CNOT(q0, q1), cirq.measure(q0, q1, key="result")) noise_levels = [0.0, 0.001, 0.01, 0.05, 0.1] results = noise_comparison_study(circuit, noise_levels) ``` ## Best Practices 1. **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 2. **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) 3. **Hardware Execution** - Always test on simulators first - Select best qubits using calibration data - Optimize circuits for target hardware gateset - Validate any mitigation against held-out calibration and uncertainty - Store expensive hardware results immediately 4. **Circuit Optimization** - Start with high-level built-in transformers - Chain multiple optimizations in sequence - Track depth and gate count reduction - Validate correctness after transformation 5. **Noise Modeling** - Use realistic noise models from calibration data - State which gate, idle, decoherence, and readout effects the model includes - Characterize before mitigating - Keep circuits shallow to minimize noise accumulation 6. **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 - **Official Documentation**: https://quantumai.google/cirq - **API Reference**: https://quantumai.google/reference/python/cirq - **Tutorials**: https://quantumai.google/cirq/tutorials - **Examples**: https://github.com/quantumlib/Cirq/tree/main/examples - **Version policy**: https://quantumai.google/cirq/dev/versions - **ReCirq**: https://github.com/quantumlib/ReCirq ## Common Issues **Circuit too deep for hardware:** - Use circuit optimization transformers to reduce depth - See `transformation.md` for optimization techniques **Memory issues with simulation:** - For suitable channels, use state-vector trajectories and average enough independent runs; a single trajectory is not the mixed-state density matrix - 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 ## Citing Scientific Agent Skills This skill is part of Scientific Agent Skills by K-Dense. 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