Non-von Neumann computing using networks of optical parametric oscillators
Combinatorial optimization problems are central in numerous important application areas, including operations and scheduling, drug discovery, finance, circuit design, sensing, and manufacturing. There is a long history of attempts to find alternatives to current von Neumann-computer-based methods for solving such problems, including neural networks, DNA computing, and most recently adiabatic quantum computation and quantum annealing.
Networks of coupled optical parametric oscillators (OPOs) are an alternative physical system, with an unconventional operating mechanism, for solving the Ising problem, which is an NP-hard optimization problem. We have realized a fully-programmable 100-spin Ising machine using a network of OPOs, and with it can solve many different Ising problem instances. Our design supports all-to-all connectivity among the implemented spins via a combination of time-division multiplexing and measurement feedback.
In this talk I will describe our work on constructing Ising machines using OPO networks with feedback, and will present the experimental results from our first prototype system.
 P.L. McMahon, et al. Science 354, 6312, pp. 614-617 (2016).
Bio: Peter McMahon received his Ph.D. in Electrical Engineering in 2014 from Stanford University in the group of Yoshihisa Yamamoto. His graduate work was on the development of building blocks for quantum computers and quantum repeaters using semiconductor systems. He subsequently began a postdoctoral appointment jointly in the groups of Hideo Mabuchi and Yoshihisa Yamamoto, working on the development of hybrid optical-electronic computing machines using principles and techniques borrowed from quantum optics.