quantumlib/Cirq

Python framework for creating, editing, and running Noisy Intermediate-Scale Quantum (NISQ) circuits.

What it solves

Cirq is a Python package designed to write, manipulate, and run quantum circuits on both quantum computers and simulators. It specifically addresses the challenges of Noisy Intermediate-Scale Quantum (NISQ) computers, where hardware-specific details are critical for achieving high-performance results.

How it works

Cirq provides a set of abstractions for quantum computing, allowing users to define flexible gates, create parameterized circuits with symbolic variables, and model hardware devices and noise. It includes built-in simulators to test circuits before running them on actual hardware, and integrates with qsim for high-performance simulation.

Who it’s for

Quantum researchers, developers, and scientists who need to build and simulate quantum programs, particularly those working with NISQ-era hardware.

Highlights

  • Hardware-Aware Design: Focuses on the specific constraints and noise profiles of real quantum hardware.
  • Circuit Optimization: Tools for circuit transformation, compilation, and optimization.
  • Customization: Supports flexible gate definitions and custom gates.
  • Interoperability: Works seamlessly with NumPy and SciPy.
  • Broad Ecosystem: Integrates with other Google Quantum AI tools like TensorFlow Quantum for machine learning and OpenFermion for chemistry.

Related

  • Project
    qulacs/qulacs

    A high-performance Python/C++ library for the fast simulation of large, noisy, or parametric quantum circuits, supporting both CPU multi-threading and GPU acceleration.

  • Project
    quantumlib/qsim

    A high-performance state-vector simulator for quantum circuits that uses hardware acceleration and gate fusion to simulate quantum state evolution.

  • Project
    Classiq/classiq-library

    An end-to-end platform for designing, optimizing, and executing quantum software that transforms high-level functional logic into optimized quantum circuits.

  • Project
    tensorflow/quantum

    TensorFlow Quantum is a Python library that integrates Cirq quantum circuits with TensorFlow/Keras, providing high‑performance hybrid quantum‑classical machine‑learning models, automatic differentiation, and scalable simulation via qsim.

  • Project
    zxcalc/pyzx

    A Python tool for the creation, visualization, and automated simplification of large-scale quantum circuits using the ZX-calculus diagrammatic reasoning system.