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cirq

Build, simulate, and execute quantum circuits with Cirq.

21|2|Updated Dec 8, 2025
Search Tags:#quantum computing#hardware execution#circuit simulation#quantum programming#noisy simulation#circuit optimization#VQE QAOA QPE
One-click install
npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill cirq-silverstein
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cirq
Source: https://github.com/silverstein/claude-scientific-skills-desktop/tree/main/corpus/cirq
Command: npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill cirq-silverstein

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Cirq helps you build correct quantum circuits and validate their behavior through simulation and hardware-compatible compilation so you can move from algorithm design to real quantum experiments with less guesswork.

Core Features & Use Cases

  • Circuit construction and parameterization: Create circuits with common qubits, gates, custom operations, and symbolic parameters for sweeps and optimization.
  • Simulation workflows: Run state-vector and density-matrix simulations, perform parameter sweeps, and inspect results via histograms and expectation values.
  • Hardware and noise readiness: Compile and transform circuits for target gatesets and device constraints, then model noise (including decoherence and readout) for realistic benchmarking and error mitigation.
  • Core use cases: Implement and test VQE, QAOA, and QPE workflows; study noise impact; and execute experiments on providers like Google, IonQ, Azure Quantum, AQT, and Pasqal.

Quick Start

Ask Cirq to build and simulate a simple 2-qubit circuit in Python, returning measurement outcomes as a histogram.

Frequently Asked Questions about cirq

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I simulate quantum circuits with noise models for realistic benchmarking?▼

Quantum circuit simulation with noise models uses density-matrix simulation to inject decoherence and readout errors, producing realistic benchmarking results for error mitigation analysis.

What is the best way to implement VQE and QAOA workflows for quantum algorithm development?▼

Implementing VQE and QAOA workflows requires building parameterized quantum circuits with symbolic sweeps, then running state-vector simulations to evaluate expectation values across optimization steps.

Can I execute quantum circuits directly on hardware providers like Google, IonQ, and Azure Quantum?▼

Executing quantum circuits on hardware providers requires compiling and transforming circuits for target gatesets and device constraints, then routing to provider-specific quantum devices for end-to-end experiments.

How do I run parameter sweeps across quantum circuits to optimize gate operations?▼

Parameter sweeps across quantum circuits use symbolic parameters embedded in gate operations, iterating simulation runs across variable ranges to inspect measurement histograms and optimize algorithm performance.

Does quantum circuit compilation automatically transform gates for specific hardware device constraints?▼

Quantum circuit compilation transforms circuits by mapping operations to target hardware gatesets and device topology constraints, ensuring compatibility before executing on quantum devices.

When should I use density-matrix simulation instead of state-vector simulation for quantum circuits?▼

Density-matrix simulation is required when modeling noise effects like decoherence and readout errors, whereas state-vector simulation suits noiseless ideal circuit behavior analysis.

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