Abstract
Many quantum computation algorithms utilizing quantum mechanical behavior such as superposition of quantum states have been proposed.
Recently, Farhi et al. have proposed an adiabatic quantum computation (AQC), which can be applied to various problems including non-deterministic polynomial time problems if one can know an appropriate Hamiltonian for a target problem.
We have proposed a neuromorphic adiabatic quantum computation (NAQC) as the AQC with energy dissipation and an efficient method for designing a final Hamiltonian in consideration of the analogy with a neural network.
The NAQC can be applied to optimization problems if its cost function can be expressed in a quadratic form.
And successful operations have been confirmed by numerical simulations.
In addition, we have proposed a new learning method for the NAQC inspired by Hebb learning and have shown its successful results for the network with four quantum bits.
In this paper, we show preliminary results for a quantum bit network with NAQC and the proposed learning by numerical simulations.
And we discuss its capability as an associative memory.
