What is the Cirq skill for?

It provides specialized guidance for writing Python code using Google's Cirq library to build, simulate, and execute quantum algorithms.

How do I optimize circuits for specific hardware?

The skill provides patterns for using Cirq's transformer framework to perform gate decomposition, qubit routing, and depth reduction.

Does it support noise simulation?

Absolutely. It covers noise channels, constant and gate-specific noise models, and density matrix simulations for realistic quantum experiments.

Can I run circuits on real hardware with this skill?

Yes, it includes implementation patterns for connecting to Google Quantum AI, IonQ, AQT, Pasqal, and Azure Quantum backends.

Cirq Quantum Computing

Name: Cirq Quantum Computing
Author: Activer007

byActiver007

データサイエンスとML

Builds, simulates, and optimizes quantum circuits for Google Quantum AI and other leading hardware providers.

概要

This skill provides comprehensive guidance for working with Cirq, Google's open-source framework for NISQ (Noisy Intermediate-Scale Quantum) computers. It enables users to design complex quantum circuits, perform high-fidelity simulations with noise modeling, and optimize algorithms for execution on real quantum hardware like Sycamore or IonQ. Whether you are implementing Variational Quantum Algorithms (VQA), exploring quantum error mitigation, or benchmarking processor performance, this skill offers the specific patterns and best practices needed to navigate the quantum computing stack efficiently.

主な機能

0 GitHub stars
Noisy and noiseless state vector and density matrix simulation
Multi-provider integration (Google, IonQ, Azure Quantum, Pasqal)
Quantum circuit construction and gate decomposition
Comprehensive noise modeling and characterization tools
Hardware-native circuit optimization and transformation pipelines

ユースケース

Compiling abstract quantum circuits for specific processor topologies
Developing Variational Quantum Algorithms like VQE and QAOA
Simulating hardware-specific noise to predict experimental outcomes

概要

主な機能

0 GitHub stars
Noisy and noiseless state vector and density matrix simulation
Multi-provider integration (Google, IonQ, Azure Quantum, Pasqal)
Quantum circuit construction and gate decomposition
Comprehensive noise modeling and characterization tools
Hardware-native circuit optimization and transformation pipelines

ユースケース

Compiling abstract quantum circuits for specific processor topologies
Developing Variational Quantum Algorithms like VQE and QAOA
Simulating hardware-specific noise to predict experimental outcomes