Sequential Bayesian Experiment Design for Optically Detected Magnetic Resonance of Nitrogen-Vacancy Centers

Abstract

With a room-temperature coherence time as long as milliseconds, the nitrogen-vacancy (N-$V$) center in diamond is used at the leading edge in quantum computing, cryptography, and memory; biocompatible markers and drug delivery; and mechanical, thermal, and magnetic sensors. Despite this prominence, experiments with N-$V$ centers are often hampered by poor photon-collection efficiency. The authors use the machine-learning technique of $B\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}y\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}n$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}f\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}e$ to maximize the information obtained from each photon, which for example speeds up N-$V$-center magnetometry by more than an order of magnitude.

Topics & Concepts