Study on the performance of neuromorphic adiabatic quantum computation algorithms

Quantum computation algorithms indicate possibility that non-deterministic polynomial time (NP-time) problems can be solved much faster than by classical methods. Farhi et al., have proposed an adiabatic quantum computation (AQC) for solving the three-satisfiability problem (3-SAT). We have proposed a neuromorphic quantum computation algorithm based on AQC, in which an analogy to an artificial neural network (ANN) is considered in order to design a Hamiltonian. However, in the neuromorphic AQC, the relation between its computation time and the probability of correct answers is not clear yet. In this paper, we study both of residual energy and the probability of finding solution as a function of computation time. The results show that the performance of the neuromorphic AQC depends on the characteristic of Hamiltonians.