QSAN: A Near-Term Achievable Quantum Self-Attention Network

Abstract

Self-attention mechanism (SAM) is good at capturing the intrinsic connection between features to dramatically boost the performance of machine learning models. Nevertheless, the capability of SAM is not equipped with many current quantum machine learning (QML) models, thus confining their expansion on massive high-dimensional quantum data. To address the above problems, a quantum SAM (QSAM) consisting of a quantum logic similarity (QLS)-based quantum bit self-attention score matrix (QBSASM) is introduced to augment the data representation of SAM exponentially. According to QSAM, the framework and quantum circuits of a one-step achievable quantum self-attention network (QSAN) are designed to consider measurement times compression fully. Moreover, a prototype of quantum coordinates is presented during the design process to describe the mathematical relationship between the output bits and the control bits to facilitate the programming. Ultimately, MNIST binary classification experiments on the PennyLane platform and comparisons with cutting-edge QML models demonstrate QSAN converges about $1.7\times $ and $2.3\times $ faster than hardware-efficient ansatz and quantum approximate optimization algorithm (QAOA) ansatz, respectively, with similar parameter configurations and 100% prediction accuracy, which indicates that it has a better learning capability. In the CIFAR-10 classification experiments, QSAN achieves high prediction accuracy at a small scale relative to classical machine learning models. Predictably, QSAN elevates the efficiency of QML models and lays the foundation for future quantum computers to perform machine learning on massive amounts of data while promoting the advancement of quantum computer vision and other fields.