Can I use this for noisy quantum simulations?

Yes, it includes comprehensive support for density matrix simulation, noise channels (depolarizing, amplitude/phase damping), and noise characterization techniques like randomized benchmarking.

Does it support parameterized circuits for variational algorithms?

Yes, it includes patterns for using SymPy symbols within circuits, enabling parameter sweeps and optimization loops for algorithms like QAOA and VQE.

How does this skill help with circuit optimization?

It provides implementation patterns for Cirq's transformer framework, including gate decomposition, merging operations, and qubit routing to reduce circuit depth for hardware execution.

What hardware providers does the Cirq skill support?

The skill supports integration with Google Quantum AI (Sycamore/Weber), IonQ, Alpine Quantum Technologies (AQT), Pasqal, and Azure Quantum backends.

Cirq Quantum Computing

Name: Cirq Quantum Computing
Author: Microck

byMicrock

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Ciencia de Datos y ML

Designs, simulates, and executes quantum circuits on simulators and real quantum hardware using Google's Cirq framework.

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The Cirq skill for Claude Code provides specialized assistance for building, optimizing, and running quantum algorithms. It helps developers construct complex quantum circuits using various qubit topologies, perform high-fidelity simulations with sophisticated noise modeling, and prepare circuits for execution on diverse hardware providers like Google Sycamore, IonQ, and Pasqal. This skill is indispensable for researchers and developers working on NISQ-era algorithms like VQE and QAOA, offering patterns for circuit decomposition, gate optimization, and hardware-specific compilation workflows.

Características Principales

Comprehensive noise modeling for realistic quantum experiment emulation
81 GitHub stars
Advanced simulation capabilities including state vector and density matrix modes
Quantum circuit construction with multi-qubit gates and symbolic parameters
Direct integration with Google Quantum AI, IonQ, and Azure Quantum backends
Automated circuit optimization and hardware-native gate decomposition

Casos de Uso

Modeling environmental noise and decoherence to benchmark algorithm robustness
Optimizing circuit depth and gate count for specific quantum hardware constraints
Developing and testing Variational Quantum Eigensolver (VQE) algorithms

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Características Principales

Comprehensive noise modeling for realistic quantum experiment emulation
81 GitHub stars
Advanced simulation capabilities including state vector and density matrix modes
Quantum circuit construction with multi-qubit gates and symbolic parameters
Direct integration with Google Quantum AI, IonQ, and Azure Quantum backends
Automated circuit optimization and hardware-native gate decomposition

Casos de Uso

Modeling environmental noise and decoherence to benchmark algorithm robustness
Optimizing circuit depth and gate count for specific quantum hardware constraints
Developing and testing Variational Quantum Eigensolver (VQE) algorithms