针对异构量子设备的联邦学习新方法，提升训练精度与兼容性。
Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus
- 按层设计损失函数，适配不同深度量子电路
- 实测和仿真均使测试准确率平均提升12.4%
- 支持低比特、少采样、中段测量等多样化需求
量子机器学习（QML）有望解决经典计算无法处理的问题，但关键数据分散在多个私有客户端，亟需以量子联邦学习（QFL）形式实现分布式训练。然而，各客户端使用的量子计算机存在误差且特性各异，导致可运行的量子线路深度不同。本文提出新方案Quorus，采用分层损失函数，有效训练不同深度的量子模型，使客户端可根据自身能力选择高保真输出的模型。Quorus还提供多种基于客户端需求的模型设计，优化了采样次数、量子比特数量、中段测量及优化空间。仿真与真实硬件实验表明，该方法显著提升了高深度客户端的梯度幅值，并在测试准确率上相较现有最优方法平均提高12.4%。
原文摘要 · Abstract (English)
Quantum machine learning (QML) holds the promise to solve classically intractable problems, but, as critical data can be fragmented across private clients, there is a need for distributed QML in a quantum federated learning (QFL) format. However, the quantum computers that different clients have access to can be error-prone and have heterogeneous error properties, requiring them to run circuits of different depths. We propose a novel solution to this QFL problem, Quorus, that utilizes a layerwise loss function for effective training of varying-depth quantum models, which allows clients to choose models for high-fidelity output based on their individual capacity. Quorus also presents various model designs based on client needs that optimize for shot budget, qubit count, midcircuit measurement, and optimization space. Our simulation and real-hardware results show the promise of Quorus: it increases the magnitude of gradients of higher depth clients and improves testing accuracy by 12.4% on average over the state-of-the-art.
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