Experimental Quantum-Enhanced Machine Learning in Spin-Based Systems

With the advancement of computing power and algorithms, machine learning has been a powerful tool in numerous applications nowadays. However, the hardware limitation of classical computers and the increasing size of datasets urge the community to explore new techniques for machine learning. Quantum-enhanced machine learning is such a rapidly growing field. It refers to quantum algorithms that are implemented in quantum computers, which can improve the computational speed of classical machine learning tasks and often promises an exponential speedup. In the past few years, the development of experimental quantum technologies leads to many experimental demonstrations of quantum-enhanced machine learning in diverse physical systems. Here, the recent experimental progress in this field in two typical spin-based quantum systems-nuclear magnetic resonance and nitrogen-vacancy centers in diamond-is reviewed, and the ongoing challenges are discussed.

Automated summary

Machine learning has become a powerful tool with the advancement of computing power and algorithms, leading to the rapidly growing field of quantum-enhanced machine learning. The development of experimental quantum technologies has enabled many experimental demonstrations of quantum-enhanced machine learning in diverse physical systems.