Numerous current Quantum Machine Learning (QML) models exhibit an inadequacy
in discerning the significance of quantum data, resulting in diminished
efficacy when handling extensive quantum datasets. Hard Attention Mechanism
(HAM), anticipated to efficiently tackle the above QML bottlenecks, encounters
the substantial challenge of non-differentiability, consequently constraining
its extensive applicability. In response to the dilemma of HAM and QML, a
Grover-inspired Quantum Hard Attention Mechanism (GQHAM) consisting of a
Flexible Oracle (FO) and an Adaptive Diffusion Operator (ADO) is proposed.
Notably, the FO is designed to surmount the non-differentiable issue by
executing the activation or masking of Discrete Primitives (DPs) with Flexible
Control (FC) to weave various discrete destinies. Based on this, such discrete
choice can be visualized with a specially defined Quantum Hard Attention Score
(QHAS). Furthermore, a trainable ADO is devised to boost the generality and
flexibility of GQHAM. At last, a Grover-inspired Quantum Hard Attention Network
(GQHAN) based on QGHAM is constructed on PennyLane platform for Fashion MNIST
binary classification. Experimental findings demonstrate that GQHAN adeptly
surmounts the non-differentiability hurdle, surpassing the efficacy of extant
quantum soft self-attention mechanisms in accuracies and learning ability. In
noise experiments, GQHAN is robuster to bit-flip noise in accuracy and
amplitude damping noise in learning performance. Predictably, the proposal of
GQHAN enriches the Quantum Attention Mechanism (QAM), lays the foundation for
future quantum computers to process large-scale data, and promotes the
development of quantum computer vision.

提出了一种基于格罗弗启发的量子硬注意机制，利用灵活的预言机和自适应扩散算子来提高量子机器学习的性能和适用性，在 Fashion MNIST 二分类任务中展示了优于传统方法的准确性和学习能力。