The past decade has seen considerable progress in quantum hardware in terms of the speed, number of qubits and quantum volume which is defined as the maximum size of a quantum circuit that can be effectively implemented on a near-term quantum device. Consequently, there has also been a rise in the number of works based on the applications of Quantum Machine Learning (QML) on real hardware to attain quantum advantage over their classical counterparts. In this survey, our primary focus is on selected supervised and unsupervised learning applications implemented on quantum hardware, specifically targeting real-world scenarios. Our survey explores and highlights the current limitations of QML implementations on quantum hardware. We delve into various techniques to overcome these limitations, such as encoding techniques, ansatz structure, error mitigation, and gradient methods. Additionally, we assess the performance of these QML implementations in comparison to their classical counterparts. Finally, we conclude our survey with a discussion on the existing bottlenecks associated with applying QML on real quantum devices and propose potential solutions for overcoming these challenges in the future.

翻译：过去十年间，量子硬件在速度、量子比特数量及量子体积（即能在近期量子设备上有效实现的最大量子电路规模）方面取得了显著进展。随之而来的是，基于量子机器学习（QML）在真实硬件上实现相对于经典方法量子优势的研究数量也呈上升趋势。本综述重点关注在量子硬件上实现的特定监督与无监督学习应用，尤其针对实际场景。我们深入探究并指出了当前QML在量子硬件上实现所面临的局限，系统综述了克服这些局限的各种技术，包括编码方法、拟设结构、误差缓解及梯度方法。此外，我们对比评估了这些QML实现相较于经典方法的性能表现。最后，本综述就当前在真实量子设备上应用QML时存在的瓶颈展开讨论，并为未来克服这些挑战提出了潜在解决方案。