ProjectQ is a comprehensive quantum computing framework that enables users to design, simulate, optimize, and execute quantum circuits across multiple hardware backends. The framework features a modular compiler engine pipeline that transforms high-level quantum operations into hardware-specific gate sequences through decomposition rules and optimization passes. It provides abstraction layers for various quantum hardware (IBM Quantum, AQT, AWS Braket, Azure Quantum, IonQ) and includes a high-performance simulator with C++ backends. Key capabilities include resource estimation for circuit optimization, quantum algorithm libraries (arithmetic, Fourier transform), circuit visualization via LaTeX/TikZ, and meta-programming constructs for reversible computing. The framework is actively maintained with recent updates to dependencies, CI/CD pipelines, and backend integrations.
Dynamiqs is a high-performance Python library for simulating quantum systems, built on JAX for GPU acceleration and automatic differentiation. It provides solvers for the Schrödinger equation, Lindblad master equation, stochastic master equations (diffusive and jump), and Floquet integration, with support for time-dependent Hamiltonians, batching over parameters, and gradient computation (including Hessians). The architecture is modular: an abstract integrator hierarchy (with concrete solvers using Diffrax, matrix-exponential, low-rank, Rouchon, and Monte Carlo methods), custom quantum array types (dense and sparse diagonal DIA layout), a clean `TimeQArray` abstraction for time-dependent operators, and utilities for state/operator construction, plotting, and quantum information metrics. The library is designed for quantum optimal control and parameter estimation, and is used in production at Alice & Bob. It features extensive automated tests (including numerical gradient/Hessian validation) and is actively maintained with a growing community.