Cirq V2

Cirq - Quantum Computing with Python

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/cirq from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

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.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Capabilities, Common Patterns, Common Issues, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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.
• Use when the request clearly matches the imported source intent: Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. bash uv pip install cirq For hardware integration: bash # 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
2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: 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

Imported: 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, see:

• 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

Examples

Example 1: Ask for the upstream workflow directly

Use @cirq-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @cirq-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @cirq-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @cirq-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: 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}")

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• 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)

Imported Operating Notes

Imported: 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
   • Implement error mitigation for production runs
   • 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
   • Include all error sources (gate, decoherence, readout)
   • 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

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/cirq, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: 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/master/examples
• ReCirq: https://github.com/quantumlib/ReCirq

Imported: 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

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
        # Setup workspace...
        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:
        # 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)

Imported: 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

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.