Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages

TL;DR

This paper introduces two transfer fusion frameworks for quantum machine learning that align data distributions across domains, leveraging quantum advantages and demonstrating state-of-the-art results on real quantum devices.

Contribution

It proposes novel transfer fusion frameworks on quantum devices, including a theoretically quadratic speedup method and a practical NISQ-compatible architecture.

Findings

The variational transfer fusion framework achieves SOTA performance.

The QBLAS-based method offers quadratic speedup on universal quantum computers.

Numerical experiments validate effectiveness on synthetic and handwritten datasets.

Abstract

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.