English

Message-passing algorithm of quantum annealing with nonstoquastic Hamiltonian

Disordered Systems and Neural Networks 2019-05-01 v2 Machine Learning Quantum Physics Machine Learning

Abstract

Quantum annealing (QA) is a generic method for solving optimization problems using fictitious quantum fluctuation. The current device performing QA involves controlling the transverse field; it is classically simulatable by using the standard technique for mapping the quantum spin systems to the classical ones. In this sense, the current system for QA is not powerful despite utilizing quantum fluctuation. Hence, we developed a system with a time-dependent Hamiltonian consisting of a combination of the formulated Ising model and the "driver" Hamiltonian with only quantum fluctuation. In the previous study, for a fully connected spin model, quantum fluctuation can be addressed in a relatively simple way. We proved that the fully connected antiferromagnetic interaction can be transformed into a fluctuating transverse field and is thus classically simulatable at sufficiently low temperatures. Using the fluctuating transverse field, we established several ways to simulate part of the nonstoquastic Hamiltonian on classical computers. We formulated a message-passing algorithm in the present study. This algorithm is capable of assessing the performance of QA with part of the nonstoquastic Hamiltonian having a large number of spins. In other words, we developed a different approach for simulating the nonstoquastic Hamiltonian without using the quantum Monte Carlo technique. Our results were validated by comparison to the results obtained by the replica method.

Keywords

Cite

@article{arxiv.1901.06901,
  title  = {Message-passing algorithm of quantum annealing with nonstoquastic Hamiltonian},
  author = {Masayuki Ohzeki},
  journal= {arXiv preprint arXiv:1901.06901},
  year   = {2019}
}

Comments

17 pages

R2 v1 2026-06-23T07:17:28.505Z