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Photonic Probabilistic Computing


Term:

Summer

Department:

QI: MIT Quest for Intelligence

Faculty Supervisor:

Marin Soljacic

Faculty email:

soljacic@mit.edu

Apply by:

05/01/2020

Contact:

chrc@mit.edu

Project Description

Photonic machines for Machine Learning and NP-Hard Optimization have the potential to enable orders-of-magnitude faster and more efficient computing. Recently, our group has pioneered photonic hardware such as Optical Neural Networks (Nature Photonics, 2017), and Photonic Recurrent Ising Machines (Nature Communications, 2020; Optica, 2020). In the latter, we emphasized the role of photonic stochastic components to speed up the computation, and to sample complex probability distributions. We are now exploring a more general class of stochastic algorithms that can be implemented in photonic hardware, Photonic Probabilistic Computing. We offer a UROP position to a student that will: -- Develop a general theoretical framework for Photonic Probabilistic Computing. With a combination of theory and numerical simulations, the student will benchmark the algorithm against other well-known stochastic algorithms. -- Investigate several possible applications of Photonic Probabilistic Computing, and determine its potential advantages in addressing some of the following tasks: training Neural Networks, implementation Bayesian Neural Networks and stochastic Neural Networks techniques such as dropout, solving NP-Hard optimization problems, such as Ising problems and Integer Factorization. -- Suggest algorithmic methods and photonic implementations to increase the performance of the algorithm. -- In coordination with the experimental team, propose a proof-of-concept of Photonic Probabilistic Computing. References: https://www.nature.com/articles/nphoton.2017.93 https://www.nature.com/articles/s41467-019-14096-z https://arxiv.org/abs/1909.13877 https://physics.aps.org/articles/v12/61

Pre-requisites

Medium to advanced knowledge in stochastic processes and algorithms (Monte Carlo Markov Chain, simulated annealing, (Bayesian) Machine Learning, etc.). Prior knowledge in physics/photonics will also be valued. Should apply students with a strong theoretical background and a desire to engage into an active research area that combines computer science, mathematics, and physics.