English

QPanda3: A High-Performance Software-Hardware Collaborative Framework for Large-Scale Quantum-Classical Computing Integration

Programming Languages 2025-06-16 v2

Abstract

In emerging quantum-classical integration applications, the classical time cost-especially from compilation and protocol-level communication often exceeds the execution time of quantum circuits themselves, posing a severe bottleneck to practical deployment. To overcome these limitations, QPanda3 has been extensively optimized as a high-performance quantum programming framework tailored for the demands of the NISQ era and quantum-classical hybrid workflows. It features optimized circuit compilation, a custom binary instruction stream (OriginBIS), and hardware-aware execution strategies to significantly reduce latency and communication overhead. OriginBIS achieves up to 86.9×\times faster encoding and 35.6×\times faster decoding than OpenQASM 2.0, addressing critical bottlenecks in hybrid quantum systems. Benchmarks show 10.7×\times compilation speedup and up to 597×\times acceleration in compiling large-scale circuits (e.g., a 118-qubit W-state) compared to Qiskit. n high-performance simulation, QPanda3 excels in variational quantum algorithms, achieving up to 26×\times faster gradient computation than Qiskit, with minimal time-complexity growth across circuit depths. These capabilities make QPanda3 well-suited for scalable quantum algorithm development in finance, materials science, and combinatorial optimization, while supporting industrial deployment and cloud-based execution in quantum-classical hybrid computing scenarios.

Keywords

Cite

@article{arxiv.2504.02455,
  title  = {QPanda3: A High-Performance Software-Hardware Collaborative Framework for Large-Scale Quantum-Classical Computing Integration},
  author = {Tianrui Zou and Yuan Fang and Jing Wang and Menghan Dou and Jun Fu and ZiQiang Zhao and ShuBin Zhao and Lei Yu and Dongyi Zhao and Zhaoyun Chen and Guoping Guo},
  journal= {arXiv preprint arXiv:2504.02455},
  year   = {2025}
}
R2 v1 2026-06-28T22:45:05.097Z