Cirq - Quantum Computing with Python workflow skill. Use this skill when the user needs Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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name
cirq
description
Cirq - Quantum Computing with Python workflow skill. Use this skill when the user needs Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/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
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
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.
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
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 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 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 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 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.
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
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
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/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.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
