跨后端QIEO：在OpenMP5、CUDA、HIP和多语言接口间实现通用运行时可移植性

中文摘要 AI 辅助

量子启发算法通过在经典硬件上模拟量子力学原理（如叠加、干涉和概率幅演化），将候选解表示为量子比特向量，并通过旋转门算子进行演化。这种方法无需物理量子比特即可获得更高的优化性能，并已被证明在组合型、高维NP难问题上比传统求解器实现数量级加速（10–80倍）。然而，采用的一个关键障碍是缺乏一个既能提供算法性能又能提供硬件可移植性的统一执行框架。我们提出了跨后端量子启发进化优化器（QIEO），即BQP的BQPhy求解器的运行时核心，通过单一事实来源架构解决了这一差距。QIEO算法的一个C++实现针对每个硬件目标编译一次，并通过薄绑定层暴露给多种高级语言。该框架在运行时调度到CPU（顺序）、OpenMP 5（多核）、CUDA（NVIDIA）和HIP（AMD）后端，使内核适应每个设备的内存层次结构和warp/wavefront执行模型。该框架的实际效用通过共享相同C++运行时的绑定演示得到验证。BQPhy的Python库在MNIST上实现了88.60%的测试准确率的神经网络超参数优化。BQPhy的MATLAB工具箱在风电场布局优化上进行了测试，达到每年365,399 ± 4,552兆瓦时，这在统计上与粒子群优化无法区分，并且在32变量约束工程问题上比遗传算法高7.6%。Julia包处理Lotka–Volterra参数估计，其中BQPhy在相同残差上取代了原生Julia求解器，将平均SSE降低了2.1倍。

英文摘要

Quantum-inspired algorithms emulate quantum mechanical principles, such as, superposition, interference, and probabilistic amplitude evolution, on classical hardware by representing candidate solutions as qubit vectors and evolving them through rotation-gate operators. This approach offers higher optimization performance without physical qubits, and has been shown to achieve order-of-magnitude speedups (10--80$\times$) over traditional solvers on combinatorial, high-dimensional NP-hard problems. A critical barrier to adoption, however, is the lack of a unified execution framework that delivers both algorithmic performance and hardware portability. We present \textbf{Cross-Backend Quantum Inspired Evolutionary Optimizer (QIEO)}, the runtime core of BQP's BQPhy solver, which addresses this gap through a \emph{single-source-of-truth} architecture. One C++ implementation of the QIEO algorithm is compiled once per hardware target and exposed to multiple high-level languages via thin binding layers. The framework dispatches to CPU (sequential), OpenMP~5 (multi-core), CUDA (NVIDIA), and HIP (AMD) backends at runtime, adapting kernels to each device's memory hierarchy and warp/wavefront execution model. The framework's real-world utility is validated through binding demonstrations that share the identical C++ runtime. BQPhy's Python library is demonstrated on a neural network hyperparameter optimisation achieving 88.60\% test accuracy on MNIST. BQPhy's MATLAB's Toolkit is tested on wind farm layout optimisation attaining $365\,399 \pm 4\,552$~MWh/yr, which is statistically indistinguishable from particle swarm optimisation and $+7.6\%$ above genetic algorithms on a 32-variable constrained engineering problem. The Julia package tackles the Lotka--Volterra parameter estimation where BQPhy replaces native Julia solvers on the same residual, cutting mean SSE by $2.1\times$.

发表机构

• BQP

机构由 AI 辅助整理，请以论文原文为准。