  The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume-a metric
defining the maximum size of a quantum circuit effectively implementable on
near-term quantum devices. This progress has led to a surge in Quantum Machine
Learning (QML) applications on real hardware, aiming to achieve quantum
advantage over classical approaches. This survey focuses on selected supervised
and unsupervised learning applications executed on quantum hardware,
specifically tailored for real-world scenarios. The exploration includes a
thorough analysis of current QML implementation limitations on quantum
hardware, covering techniques like encoding, ansatz structure, error
mitigation, and gradient methods to address these challenges. Furthermore, the
survey evaluates the performance of QML implementations in comparison to
classical counterparts. In conclusion, we discuss existing bottlenecks related
to applying QML on real quantum devices and propose potential solutions to
overcome these challenges in the future.
