用量子设备对齐数据分布，实现跨域标签预测并获量子加速。
Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages
- 通过量子信息通道融合两个相关领域数据，对齐目标域分布。
- 在合成与手写数字数据集上达到当前最优量子域自适应性能。
- 支持通用量子计算机的二次加速，且可在NISQ设备上可扩展实现。
量子机器学习中标签数据稀缺是亟待解决的关键挑战。本文提出两种基于分布对齐的迁移融合框架，利用量子设备将目标域数据分布对齐至另一相关且已标注的源域，从而预测目标域标签。框架通过量子信息注入通道融合来自两个相关域的量子数据，借助后处理测量结果实现目标域预测，并在理论上获得量子优势。其中一种基于量子基本线性代数子程序（QBLAS）的实现，在通用量子计算机上可实现二次加速；另一种硬件可扩展架构则通过变分混合量子-经典流程在噪声中等规模量子（NISQ）设备上实现。数值实验在合成数据集与手写数字数据集上表明，该变分迁移融合（TF）框架达到了当前最优的量子域自适应（DA）方法性能。
原文摘要 · Abstract (English)
The scarcity of labelled data is specifically an urgent challenge in the field of quantum machine learning (QML). Two transfer fusion frameworks are proposed in this paper to predict the labels of a target domain data by aligning its distribution to a different but related labelled source domain on quantum devices. The frameworks fuses the quantum data from two different, but related domains through a quantum information infusion channel. The predicting tasks in the target domain can be achieved with quantum advantages by post-processing quantum measurement results. One framework, the quantum basic linear algebra subroutines (QBLAS) based implementation, can theoretically achieve the procedure of transfer fusion with quadratic speedup on a universal quantum computer. In addition, the other framework, a hardware-scalable architecture, is implemented on the noisy intermediate-scale quantum (NISQ) devices through a variational hybrid quantum-classical procedure. Numerical experiments on the synthetic and handwritten digits datasets demonstrate that the variatioinal transfer fusion (TF) framework can reach state-of-the-art (SOTA) quantum DA method performance.
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