Tootfinder

High-Efficiency, High-Fidelity Charge Initialization of Shallow Nitrogen Vacancy Centers in Diamond
Marjana Mahdia, Artur Lozovoi, Jared Rovny, Zhiyang Yuan, Carlos A. Meriles, Nathalie P. de Leon
https://arxiv.org/abs/2506.00707

High-Efficiency, High-Fidelity Charge Initialization of Shallow Nitrogen Vacancy Centers in Diamond
Nitrogen vacancy (NV) centers in diamond exhibit long spin coherence times, optical initialization, and optical spin readout under ambient conditions, making them excellent quantum sensors. However, the conventional scheme for charge state initialization based on off-resonant green excitation results in significant state preparation errors, typically around 30%. One method for improving charge state initialization fidelity is to use multicolor excitation, which has been demonstrated to achieve …

Someday, I want to erase everything and vanish offline, I’ve said it before and I’ll say it again.
I’m sick of being trapped in this endless cycle of digital puppetry and mental sludge, where every click feeds some shallow algorithm designed to keep you hooked and dumbed down, turning people into another batch of screen-zombies chasing cheap hits and empty dopamine hits.
I want out of this rat race of constant noise and manufactured crises, where reality is warped into a carnival…

This https://arxiv.org/abs/2505.22598 has been replaced.
initial toot: https://mastoxiv.page/@arX…

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and procedures, a wide theoretical understanding is still lacking, with the risk of suboptimal implementations. As a first step to address this gap, we provide here a complete analytic study of the widely-used Sequential Tempering procedure applied to a shallow MA…

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Luca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
https://arxiv.org/abs/2505.22598

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and procedures, a wide theoretical understanding is still lacking, with the risk of suboptimal implementations. As a first step to address this gap, we provide here a complete analytic study of the widely-used Sequential Tempering procedure applied to a shallow MA…