dynamiqs
A Python library for GPU-accelerated and differentiable quantum systems simulation built with JAX.
What is dynamiqs?
Dynamiqs is a Python library for simulating quantum systems with high performance on GPUs and automatic differentiation capabilities. It provides solvers for the Schrödinger equation, Lindblad master equation, stochastic master equation, and others, enabling efficient simulation of large quantum systems and gradient-based optimization tasks.
Quantum physicists, researchers, and developers working on quantum simulation, parameter estimation, quantum optimal control, or large-scale quantum system modeling who need GPU acceleration and differentiable solvers.
Developers choose Dynamiqs for its combination of GPU acceleration, automatic differentiation through JAX, and a QuTiP-like API, making it uniquely suited for large-scale quantum simulations and gradient-based optimization in quantum control and calibration.
Overview
High-performance quantum systems simulation with JAX (GPU-accelerated & differentiable solvers).
Use Cases
Best For
- Simulating large quantum systems with GPU acceleration
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