Qimax: Efficient quantum simulation via GPU-accelerated extended stabilizer formalism

TL;DR

This paper introduces Qimax, a GPU-accelerated parallel extended stabilizer formalism for efficient simulation of Clifford and near-Clifford quantum circuits, outperforming existing simulators in specific cases.

Contribution

The authors develop a parallelized extended stabilizer formalism optimized for GPUs, significantly improving simulation performance for certain quantum circuits.

Findings

Qimax outperforms Qiskit and Pennylane in specific scenarios.

Parallelization enables efficient handling of high-rank stabilizers.

The implementation is Python-based and demonstrates practical speedups.

Abstract

Simulating Clifford and near-Clifford circuits using the extended stabilizer formalism has become increasingly popular, particularly in quantum error correction. Compared to the state-vector approach, the extended stabilizer formalism can solve the same problems with fewer computational resources, as it operates on stabilizers rather than full state vectors. Most existing studies on near-Clifford circuits focus on balancing the trade-off between the number of ancilla qubits and simulation accuracy, often overlooking performance considerations. Furthermore, in the presence of high-rank stabilizers, performance is limited by the sequential property of the stabilizer formalism. In this work, we introduce a parallelized version of the extended stabilizer formalism, enabling efficient execution on multi-core devices such as GPU. Experimental results demonstrate that, in certain scenarios, our Python-based implementation outperforms state-of-the-art simulators such as Qiskit and Pennylane.