Machine and quantum learning for diamond-based quantum applications

TL;DR

This paper reviews how machine and quantum learning techniques are advancing diamond-based quantum technologies, improving measurement speed and accuracy crucial for quantum computing and sensing.

Contribution

It provides an analysis of recent developments and demonstrates the practical impact of machine and quantum learning on diamond quantum applications.

Findings

Enhanced measurement speed and accuracy in diamond quantum systems

Machine and quantum learning enable better handling of scarce resources like coherence time

Discussion of future potential of learning methods in quantum technologies

Abstract

In recent years, machine and quantum learning have gained considerable momentum sustained by growth in computational power and data availability and have shown exceptional aptness for solving recognition- and classification-type problems, as well as problems that require complex, strategic planning. In this work, we discuss and analyze the role machine and quantum learning are playing in the development of diamond-based quantum technologies. This matters as diamond and its optically-addressable spin defects are becoming prime hardware candidates for solid state-based applications in quantum information, computing and metrology. Through a selected number of demonstrations, we show that machine and quantum learning are leading to both practical and fundamental improvements in measurement speed and accuracy. This is crucial for quantum applications, especially for those where coherence time and signal-to-noise ratio are scarce resources. We summarize some of the most prominent machine and quantum learning approaches that have been conducive to the presented advances and discuss their potential for proposed and future quantum applications.