Machine and quantum learning for diamond-based quantum applications
At a Glance
Section titled âAt a Glanceâ| Metadata | Details |
|---|---|
| Publication Date | 2023-01-13 |
| Journal | Materials for Quantum Technology |
| Authors | Dylan G. Stone, Carlo Bradac |
| Institutions | Trent University, Université Bourgogne Franche-Comté |
| Citations | 6 |
Abstract
Section titled âAbstractâAbstract 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, as well as their limits, for proposed and future quantum applications.
Tech Support
Section titled âTech SupportâOriginal Source
Section titled âOriginal SourceâReferences
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