分而治之量子神经网络用于模块化混合计算架构

中文摘要 AI 辅助

量子机器学习正迅速崛起，成为一种有前景且可能更可持续的方法，能够补充传统的、高能耗的HPC、AI和GPU资源——特别是针对世界亟需解决的严峻全球挑战。近年来，首批量子处理器原型已进入市场，有时可通过云访问，分布式量子计算正开始从很大程度上理论化的概念转变为已展示的现实。然而，这些设备仍然存在噪声且量子比特受限。因此，设计具有缩减量子寄存器和有限数量量子门的量子机器学习模型是可取的，避免过深过宽的量子电路。在此，我们通过提出一种新颖的基于分而治之的训练协议来探索这一想法，该协议将量子神经网络拆分为更小的组件，并更好地管理其优化。我们在交织的能源与环境领域的真实数据和真实世界预测任务上对我们的方法进行基准测试，例如电动汽车充电站状态和全球空气质量指数。我们的结果为广泛应用的量子机器学习算法提出了一种设计原则，该原则可以在最先进的量子处理器上或正在开发的用于未来通过高速通信链路互连的混合基础设施的模块化量子架构中进行更可行的测试。

英文摘要

Quantum machine learning is rapidly emerging as a promising and potentially more sustainable approach that can complement traditional, energy-hungry HPC, AI, and GPU resources-particularly for the demanding global challenges the world urgently needs to address. In recent years, the first prototypes of quantum processors have reached the market, are sometimes available via the cloud, and distributed quantum computing is beginning to move from a largely theoretical concept to a demonstrated reality. However, these devices are still noisy and qubit-limited. Therefore, it is desirable to design quantum machine learning models with a reduced quantum register and a limited number of quantum gates, avoiding overly deep and wide quantum circuits. Here, we explore this idea by proposing a novel divide-et-impera-based training protocol that splits quantum neural networks into smaller components and better governs their optimization. We benchmark our approach on real data and real-world prediction tasks in intertwined energy and environmental domains, such as the status of charging stations for electric vehicles and the global air quality index. Our results suggest a design principle for quantum machine learning algorithms across a wide range of applications, which can be more feasibly tested on state-of-the-art quantum processors or within modular quantum architectures being developed for future hybrid infrastructures interconnected via high-speed communication links.

发表机构

• University of Florence（佛罗伦萨大学）
• Eni S.p.A.（埃尼公司）

机构由 AI 辅助整理，请以论文原文为准。