Pratik Brahma is currently a Ph.D. student in the EECS Department of the University of Berkeley, working under the supervision of Professor Sayeef Salahuddin. He completed his undergraduate degree at IIT Bombay where he studied quantum transport of topological insulators and neuromorphic computing. His primary research interest lies in the intersection of areas between condensed matter physics and machine learning. His goal is to use novel technologies and algorithms to solve very difficult problems like NP-hard and quantum many-body problems. In his free time Pratik likes to play tennis, badminton or cycle around Berkeley.
Research
Many-body quantum problems are difficult to solve due to exponential amount of information present in quantum many-body wavefunctions. I am currently working on to accelerate solving quantum systems like linear hydrogen atomic chains and quantum lattices by using Restricted Boltzmann Machine (a stochastic neural network) as a variational eigensolver. RBMs are inherently parallelizable due to its bipartite structure, thus making it attractive for hardware acceleration. My goal is to use the established mapping and training algorithm for other various difficult quantum and classical problems.
