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.

翻译：当前众多量子机器学习（QML）模型在辨识量子数据重要性方面存在不足，导致处理大规模量子数据集时效能降低。被预期能有效解决上述QML瓶颈的硬注意力机制（HAM）面临不可微分的显著挑战，从而限制了其广泛适用性。为应对HAM与QML的困境，本文提出了一种由灵活预言机（FO）和自适应扩散算子（ADO）组成的受Grover启发的量子硬注意力机制（GQHAM）。特别地，FO通过利用灵活控制（FC）执行离散基元（DP）的激活或屏蔽，编织多种离散命运，从而克服不可微分问题。基于此，这种离散选择可通过专门定义的量子硬注意力分数（QHAS）进行可视化。此外，设计了一种可训练的ADO以提升GQHAM的通用性和灵活性。最终，基于QGHAM的受Grover启发的量子硬注意力网络（GQHAN）在PennyLane平台上构建，用于Fashion MNIST二分类任务。实验结果表明，GQHAN巧妙克服了不可微分的障碍，在准确率和学习能力上超越了现有量子软自注意力机制。在噪声实验中，GQHAN在比特翻转噪声下的准确率和振幅阻尼噪声下的学习性能方面表现出更强的鲁棒性。可预见的是，GQHAN的提出丰富了量子注意力机制（QAM），为未来量子计算机处理大规模数据奠定基础，并推动量子计算机视觉的发展。