STABSim: A Parallelized Clifford Simulator with Features Beyond Direct Simulation

TL;DR

STABSim introduces a GPU-accelerated stabilizer simulator that significantly improves scalability and accuracy for quantum error correction simulations, enabling advanced quantum computing research beyond current CPU-based methods.

Contribution

This work presents the first GPU-accelerated tableau stabilizer simulator that outperforms CPU methods in QEC workloads and introduces new error models and optimization techniques.

Findings

Scales better than CPU methods in QEC workloads

Faster and more accurate non-unitary error modeling

Enables new quantum computing applications and optimizations

Abstract

The quantum stabilizer formalism became foundational for understanding error correction soon after the realization of the first useful quantum error correction codes. Stabilizers provide a way to describe sets of quantum states which are valid codewords within a quantum error correction (QEC) scheme. Existing stabilizer simulators are single threaded applications used to sample larger codes than is possible with other methods. However, there is an outstanding gap in the scaling and accuracy of current simulators for QEC as quantum computing exceeds hundreds of qubits, along with an under-utilization of the capabilities of highly-efficient stabilizer simulation across other quantum domains. In this work, we present the first GPU-accelerated tableau stabilizer simulator to scale better than CPU methods in QEC workloads, by trivializing Clifford gates and exploiting the large parallelism of dedicated GPUs with CUDA warp-level primitives to quickly overcome costly measurement gates. We then implement a new error model that captures non-unitarity in T1/T2 error channels much faster and with exact accuracy for most physical qubits, demonstrate a chemistry use case, and present a new Clifford+T to Pauli-Based Computing (PBC) transpilation optimization through our simulator.