QPanda3：面向大规模量子-经典计算融合的高性能软硬件协同框架
QPanda3: A High-Performance Software-Hardware Collaborative Framework for Large-Scale Quantum-Classical Computing Integration
AI总结：
针对量子-经典融合应用中经典编译与通信耗时过长的瓶颈，研究团队推出高性能量子编程框架QPanda3，通过优化编译、OriginBIS指令流等技术实现多维度性能跃升，支撑多领域量子算法开发与产业落地。
摘要：
在新兴的量子-经典融合应用中，经典计算耗时（尤其是编译和协议层通信的耗时）往往超过量子电路本身的执行时间，给实际部署带来了严重瓶颈。为突破这些限制，QPanda3作为一款高性能量子编程框架得到了全面优化，专为NISQ（含噪声中等规模量子）时代的需求以及量子-经典混合工作流量身打造。它具备优化的电路编译、自定义二进制指令流OriginBIS以及硬件感知的执行策略，可显著降低延迟与通信开销。OriginBIS的编码速度比OpenQASM 2.0最高快86.9倍，解码速度最高快35.6倍，解决了混合量子系统中的关键瓶颈问题。基准测试显示，与Qiskit相比，QPanda3的编译速度提升10.7倍，在编译大规模电路（如118量子比特的W态）时加速比最高可达597倍。在高性能仿真方面，QPanda3在变分量子算法中表现优异，梯度计算速度比Qiskit最高快26倍，且随电路深度增加的时间复杂度增长极小。这些特性使QPanda3非常适合金融、材料科学和组合优化领域的可扩展量子算法开发，同时支持量子-经典混合计算场景下的工业部署与云端执行。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
