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

TL;DR

This survey reviews the current state of quantum machine learning on near-term quantum devices, analyzing supervised and unsupervised techniques, their limitations, and potential solutions for real-world applications.

Contribution

It provides a comprehensive analysis of QML implementations on real hardware, highlighting current challenges and proposing future directions for overcoming them.

Findings

QML applications are increasingly feasible on near-term quantum devices.

Current limitations include encoding, ansatz design, and error mitigation.

Performance of QML on hardware is approaching classical methods but still faces significant hurdles.

Abstract

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.