Yuki Kawamura | Searcharxiv

arXiv2026

Minimally Destructive Fast Imaging of Single Atoms in an Optical Tweezer Array with Coherent Excitation

Ultracold neutral atoms in an optical lattice and an optical tweezer array offer highly-controllable quantum many-body systems, utilized for various quantum science and technology such as quantum computing, quantum metrology, and quantum simulation. By combining high-fidelity imaging of individual atoms, one can further enhance the capability of such experimental platforms as quantum gas microscopes, tweezer clocks, and tweezer-array-based quantum computers. In this work, we propose a minimally destructive single-atom imaging by deterministic coherent excitation of atoms with alternately applied pi-pulses from counter-propagating directions, mitigating the fundamental heating effect associated with the stochastic absorption process. Using ytterbium-174 atoms trapped in an optical tweezer array, we experimentally demonstrate fast and low-loss single-atom imaging with a discrimination fidelity of 99.89(5) % and a survival probability of 98.80(44) % in 17.6 microseconds. Importantly, our scheme exhibits the lower heating rate, about half of that of the former scheme utilizing the incoherent excitation. This fast and minimally destructive imaging scheme is beneficial for relaxing the requirement on the trap depth, thereby enabling scalable atom imaging across a wide range of quantum science platforms.

physics.atom-ph↗

arXiv2025

Trajectory Flow Matching with Applications to Clinical Time Series Modeling

Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medicine. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. To address this, we propose Trajectory Flow Matching (TFM), which trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on three clinical time series datasets both in terms of absolute performance and uncertainty prediction.

cs.LG↗