Conventional computing architectures currently have no known efficient algorithms for combinatorial optimization tasks such as the Ising problem. We present a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS), using the silicon-on-insulator process, that is capable of converging to the ground state of various four-spin graphs with high probability. The INPRIS exploits noise as a resource to speed up the ground state search and to explore larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem and, therefore, compatible with optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.

#### Speaker's Bio

Mihika Prabhu received her BS in Physics and Electrical Engineering from MIT in 2015. She was a recipient of the 2016 NSF Graduate Research Fellowship, completed an MS in Electrical Engineering and Computer Science at MIT in 2018, and is currently a PhD candidate in the Department of Electrical Engineering and Computer Science at MIT.