GPU-Accelerated Quantum Simulation of Stabilizer Circuits

TL;DR

This paper presents GPU-accelerated algorithms for simulating large stabilizer quantum circuits, achieving significant speedups and energy efficiency improvements over existing CPU and GPU simulators, enabling scalable quantum simulation for extensive measurements.

Contribution

The paper introduces novel GPU-specific parallel algorithms for stabilizer circuit simulation, significantly enhancing scalability and performance over prior CPU-based methods.

Findings

Up to 105× speedup over existing simulators

Simulated circuits with up to 180,000 qubits and 130 million gates

Over 80% energy reduction on demanding instances

Abstract

We introduce new parallel algorithms for efficiently simulating stabilizer (Clifford) circuits on GPUs, with a focus on data-parallel tableau evolution and scalable handling of projective measurements. Our approach reformulates key bottlenecks in stabilizer simulation -- such as Gaussian elimination and measurement updates -- into GPU-tailored primitives that eliminate sequential dependencies and maximize memory coalescing. We implement these techniques in QuaSARQ, a GPU-accelerated stabilizer simulator designed for large qubit counts and many-shot sampling. Across a broad benchmark suite reaching 180,000 qubits and depth 1,000 (roughly 130M gates), QuaSARQ shows substantial runtime improvements, with up to 105$\times$ speedup, and over 80% energy reduction on demanding instances. Moreover, QuaSARQ consistently outperforms Stim, a state-of-the-art CPU-optimized stabilizer simulator, as well as Qiskit-Aer (CPU/GPU), Qibo, Cirq, and PennyLane. Finally, QuaSARQ exhibits a significant advantage in many-shot sampling on large workloads. These results demonstrate that our parallel algorithms can significantly advance the scalability of stabilizer-circuit simulation, particularly for workloads involving extensive measurements and sampling.