Key points are not available for this paper at this time.
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 B0{0ex}a0{0ex}y0{0ex}e0{0ex}s0{0ex}i0{0ex}a0{0ex}n i0{0ex}n0{0ex}f0{0ex}e0{0ex}r0{0ex}e0{0ex}n0{0ex}c0{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.
Dushenko et al. (2020) studied this question.