analiza aiqbench
This research applies reinforcement learning to optimize nitrogen-vacancy center thermometry, significantly reducing laser-induced heating while improving spin polarization efficiency. By integrating physical models with a Progressive Action-Space Q-learning algorithm, the study demonstrates a viable path toward more stable and energy-efficient quantum sensing in heat-sensitive environments. The methodology offers a practical framework for enhancing the performance of existing quantum sensors without relying on high-power excitation sources.
Adaptive Self-Optimization of Polarization Efficiency for Low-Power NV Center Thermometers Based on PAS-Q
arXiv:2611.04588v1 Announce Type: new Abstract: Nitrogen-vacancy (NV) centers in diamond have attracted considerable attention for micro- and nanoscale thermometry owing to their high precision and spatial resolution. However, achieving sufficient electron spin polarization and signal-to-noise ratio (SNR) typically requires high-power or long-duration laser excitation, which introduces heating and degrades measurement accuracy and stability. To address this trade-off, a reinforcement learning (RL)-based approach is proposed to optimize spin polarization under low-heating constraints. First, a physical model integrating five-level optical pumping rates and NV spin polarization dynamics is established. A temperature-rise constraint model is then developed based on pulsed optically detected magnetic resonance (P-ODMR) measurements under varying laser powers and pulse widths in an external magnetic field. Subsequently, a Progressive Action-Space Q-learning (PAS-Q) algorithm is employed to explore the parameter space and identify optimal laser power and pulse-width combinations. The agent is trained through numerical simulations and the resulting strategy is implemented in a hardware system. Experimental results demonstrate a 44% reduction in laser-induced heating, a 33% increase in spin polarization rate based on T1 relaxation analysis, and a 71% reduction in optimization time compared with traditional methods. This approach enables efficient spin polarization with reduced laser heating and improved optimization efficiency, and may be extended to other heat-sensitive quantum spin sensors, including silicon-vacancy centers in diamond and divacancy defects in silicon carbide.
Abstract:Nitrogen-vacancy (NV) centers in diamond have attracted considerable attention for micro- and nanoscale thermometry owing to their high precision and spatial resolution. However, achieving sufficient electron spin polarization and signal-to-noise ratio (SNR) typically requires high-power or long-duration laser excitation, which introduces heating and degrades measurement accuracy and stability. To address this trade-off, a reinforcement learning (RL)-based approach is proposed to optimize spin polarization under low-heating constraints. First, a physical model integrating five-level optical pumping rates and NV spin polarization dynamics is established. A temperature-rise constraint model is then developed based on pulsed optically detected magnetic resonance (P-ODMR) measurements under varying laser powers and pulse widths in an external magnetic field. Subsequently, a Progressive Action-Space Q-learning (PAS-Q) algorithm is employed to explore the parameter space and identify optimal laser power and pulse-width combinations. The agent is trained through numerical simulations and the resulting strategy is implemented in a hardware system. Experimental results demonstrate a 44% reduction in laser-induced heating, a 33% increase in spin polarization rate based on T1 relaxation analysis, and a 71% reduction in optimization time compared with traditional methods. This approach enables efficient spin polarization with reduced laser heating and improved optimization efficiency, and may be extended to other heat-sensitive quantum spin sensors, including silicon-vacancy centers in diamond and divacancy defects in silicon carbide.
arXiv:2611.04588 [quant-ph]
(or arXiv:2611.04588v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2611.04588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 307 cuvinte