Experimental demonstration of quantum-enhanced machine learning in a nitrogen-vacancy-center system
At a Glance
Section titled āAt a Glanceā| Metadata | Details |
|---|---|
| Publication Date | 2020-01-09 |
| Journal | Physical review. A/Physical review, A |
| Authors | Xiaolong Ouyang, Xianzhi Huang, Yukai Wu, Weige Zhang, Xunuo Wang |
| Institutions | Tsinghua University |
| Citations | 15 |
Abstract
Section titled āAbstractāWe demonstrate the quantum-enhanced supervised classification of vectors in a nitrogen-vacancy ($NV$)ācenter system using the algorithm of Lloyd, Mohseni, and Rebentrost (arXiv:1307.0411). A $^{13}\mathrm{C}$ nuclear spin is employed to encode the vectors with the spin-triplet state of the $NV$ center as an ancilla. We design efficient methods to prepare the initial electron-nuclear entangled state within the coherence time ${T}_{2}^{*}$ of the electron spin, and then compute the distance between the test vector and the center of each class by measuring the level population through maximum likelihood estimation. Our experiment allows more than one reference vector in each class, thus forming an important enabling step toward quantum-enhanced machine learning.
Tech Support
Section titled āTech SupportāOriginal Source
Section titled āOriginal SourceāReferences
Section titled āReferencesā- 2014 - Introduction to Machine Learning
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