Quantum classifiers for domain adaptation

Abstract

Transfer learning (TL), a crucial subfield of machine learning, aims to accomplish a task in the target domain with the acquired knowledge of the source domain. Specifically, effective domain adaptation (DA) facilitates the delivery of the TL task where all the data samples of the two domains are distributed in the same feature space. In this paper, two quantum implementations of the DA classifier are presented with quantum speedup compared with the classical DA classifier. One implementation, the quantum basic linear algebra subroutines (QBLAS)-based classifier, can predict the labels of the target domain data with logarithmic resources in the number and dimension of the given data. The other implementation efficiently accomplishes the DA task through a variational hybrid quantum-classical procedure.

Multipartite entanglement serves as a faithful detector for quantum phase transitions

“…Subsequently, many detectors borrowed from quantum information science are used to characterize quantum criticality. These include the von Neumann entropy [19,20], entanglement of formation (EOF) [21][22][23], quantum fidelity [24,25], quantum discord (QD) [26,27], quantum coherence (QC) based on Wigner and Yanase skewed information [28][29][30], steered QC [31], and magic resource [32,33]. However, these proposed detectors have limitations in detecting certain types of QPTs [29,30,34,35], such as in detecting the Berezinskii-Kosterlitz-Thouless (BKT)-type QPT in the XXZ model.…”