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The past decade has witnessed significant advancements in quantum hardware,encompassing improvements in speed, qubit quantity, and quantum volume-a metricdefining the maximum size of a quantum circuit effectively implementable onnear-term quantum devices. This progress has led to a surge in Quantum MachineLearning (QML) applications on real hardware, aiming to achieve quantumadvantage over classical approaches. This survey focuses on selected supervisedand unsupervised learning applications executed on quantum hardware,specifically tailored for real-world scenarios. The exploration includes athorough analysis of current QML implementation limitations on quantumhardware, covering techniques like encoding, ansatz structure, errormitigation, and gradient methods to address these challenges. Furthermore, thesurvey evaluates the performance of QML implementations in comparison toclassical counterparts. In conclusion, we discuss existing bottlenecks relatedto applying QML on real quantum devices and propose potential solutions toovercome these challenges in the future.

Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications