Python library speeds up steady-state simulation of open quantum systems
PQLS: A High-Performance Python Library for Steady-State Simulation of Open Quantum Systems
Software Engineering
Summary
Simulating how quantum systems interact with their environment is important but computationally hard. The authors developed PQLS, a Python tool that helps find steady behaviors of these open quantum systems much faster by using hardware acceleration and smart batching of calculations. It offers user-friendly ways to describe the system at different detail levels, making it easier for both physicists and programmers to use. The tool outperforms existing software on CPUs and GPUs, especially when exploring many settings at once.
What this means in practice
- •For quantum optics engineers: Compute steady-state responses of atomic systems across many parameters quickly for designing quantum devices.
- •For photonics hardware developers: Perform large-scale simulations of quantum components to optimize device performance under varied operating conditions.
Authors
Evan Simanovskis, Raviraj Adve, Javane Rostampoor
Abstract
PQLS (Parallel Quantum Liouvillian Solver) is a high-performance Python library for computing steady-state solutions of the Lindblad master equation. It provides a layered user-facing API with three levels of abstraction. The high-level API accepts physical descriptions of atomic ladder systems and automatically determines the required model parameters; the mid- level API accepts quantities such as Rabi frequencies, detunings, decay rates, and network topology; finally the low-level API allows users to directly specify the Hamiltonian and collapse operators. This layered design enables users to choose between physical convenience and direct numerical control depending on the application. PQLS is built on JAX and XLA to enable vectorized and hardware-accelerated computation of large parameter sweeps. Rather than solving the steady-state problem sequentially for each parameter configuration, PQLS represents multiple Hamiltonians as batched tensors and processes them through a JAX-compiled solver, reducing Python-level overhead and improving hardware utilization. This is particularly useful for applications requiring large or multidimensional parameter sweeps, such as computing frequency spectra, evaluating field-dependent responses, and performing Doppler averaging over atomic velocity distributions. PQLS has been benchmarked against QuTiP, QuTiP-JAX, and RydIQule on CPU and GPU platforms, demonstrating substantial computational speedups across the tested configurations.