Quadratic Unconstrained Binary Optimization (QUBO) on neuromorphic computing system

Abstract

The problems of Artificial intelligence (AI) naturally maps to NP-hard optimization problems. This trend has significance to achieve human-level computation capability from machines. This computational ability can be achieved by developing evolutionary algorithms or mapping those evolutionary algorithms onto new generation computing systems: Quantum or Neuromorphic hardware. In this paper, we implemented the NP-hard optimization problem called Quadratic Unconstrained Binary Optimization (QUBO) problem for the solution of graph problems on the IBM's Neurosynaptic TrueNorth System. We have experimented on different types of graph problems with different levels of complexities and achieved encouraging results on IBM's Neuromorphic TrueNorth chip. Moreover, there are a set of potential applications have been discussed based on this proposed QUBO solution. Along with the QUBO on quantum annealing, it is the important first step towards the solutions of QUBO on Neuromorphic computing systems.