The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications. Given its immense potential, quantum-based computing systems have attracted increasing attention with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting.

翻译：量子计算的快速发展将经典设计推向量子领域，突破了计算密集型与数据密集型应用的物理极限。凭借其巨大潜力，基于量子的计算系统日益受到关注，部分系统有望提供量子加速效应。例如，变分量子算法已被提出用于量子神经网络，在量子比特上训练深度学习模型并取得了令人瞩目的成果。现有量子学习架构与系统依赖于单一、单体式且资源（如量子比特）充足稳定的量子机器。然而，制造大型单体量子器件的难度远高于生产小型器件阵列。本文研究了一种将多台量子机器整合为统一系统的分布式量子系统。我们提出DQuLearn，将量子学习任务分解为多个子任务，每个子任务可分布式地在独立量子机器上执行，其结果回传至经典机器以进行后续训练迭代。此外，该系统支持多并发客户端，并能根据量子工作节点的运行时状态动态管理其量子线路。通过大量实验证明，DQuLearn在4个工作节点的多租户场景下，实现了与单机相当的精度，同时运行时间显著降低高达68.7%，每秒电路处理速度提升至3.99倍。