Machine and quantum learning for diamond-based quantum applications
- URL: http://arxiv.org/abs/2208.00256v1
- Date: Sat, 30 Jul 2022 15:36:26 GMT
- Title: Machine and quantum learning for diamond-based quantum applications
- Authors: Dylan G. Stone and Carlo Bradac
- Abstract summary: We discuss and analyze the role machine and quantum learning are playing in the development of diamond-based quantum technologies.
We show that machine and quantum learning are leading to both practical and fundamental improvements in measurement speed and accuracy.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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