Seou Choi

Jan 19, 2025 | nature.com | Michael Horodynski |Yannick Salamin |Seou Choi

AbstractOptical computing often employs tailor-made hardware to implement specific algorithms, trading generality for improved performance in key aspects like speed and power efficiency. An important computing approach that is still missing its corresponding optical hardware is probabilistic computing, used e.g. for solving difficult combinatorial optimization problems. In this study, we propose an experimentally viable photonic approach to solve arbitrary probabilistic computing problems.

Sep 4, 2024 | nature.com | Seou Choi |Yannick Salamin |Charles Roques-Carmes |Rumen Dangovski |Di Luo |Zhuo Chen | +2 more

AbstractProbabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited.