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

2025-05-06

AI Summary

Simulating high-rank stabilizer-based near-Clifford quantum circuits on multicore devices (e.g., GPUs) suffers from low efficiency and high resource overhead. Method: This work proposes and implements, for the first time, a parallelized architecture based on an extended stabilizer formalism, breaking away from conventional serial update paradigms. It integrates GPU acceleration, CUDA optimization, multithreaded stabilizer updates, and parallel sampling algorithms. Contribution/Results: Implemented in Python, the approach outperforms mainstream simulators—including Qiskit and PennyLane—on diverse near-Clifford circuits, achieving speedups of several-fold to an order of magnitude. It significantly improves throughput and scalability for high-rank scenarios, establishing an efficient, scalable new paradigm for large-scale near-Clifford circuit simulation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Quantum Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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.

Problem

• Efficient quantum simulation using GPU-accelerated extended stabilizer formalism
• Improving performance in near-Clifford circuits with parallelized stabilizer methods
• Overcoming limitations of sequential stabilizer formalism for high-rank cases

Innovation

• GPU-accelerated extended stabilizer formalism
• Parallelized execution on multi-core devices
• Python-based outperforming Qiskit and Pennylane