@techreport{oai:ipsj.ixsq.nii.ac.jp:00217635,
 author = {下舞, 創平 and 木村, 晋二 and Sohei, Shimomai and Shinji, Kimura},
 issue = {13},
 month = {Mar},
 note = {量子モンテカルロ法に基づく量子アニーリングでは，相互関係のあるスピンをランダムに選んでトグルさせるかどうかを決め，スピンの遷移によってエネルギーの最小解を求める．しかし，エネルギーの収束に時間がかかったり，全域的な最適解に収束しないことがある．そこで，各トロッタのエネルギーを保持・更新しておくことで，計算途中でエネルギー最小のスピン状態を求め，その解よりも良い解が出るまで中間最小解を保持することで，同じ計算時間で解の精度を向上させる手法を提案する．, Quantum annealing is a new algorithm to solve combinatorial optimization problems where the original problem is converted to the energy minimization of Ising model or the equivalent QUBO (Quadratic Unconstrained Binary Optimization). Speeding up quantum annealing is important to obtain the solutions of combinatorial optimization problems in short time. In this manuscript, an acceleration method of simulated quantum annealing (SQA) based on the quantum Monte Carlo method is discussed and a new method is introduced to improve the quality of the solution under the same amount of computation time. The method keeps a temporally minimum solution during the computation and renews the temporally minimum solution when a better solution can be found. Its effectiveness is shown by applying maxcut problems and traveling salesman problems.},
 title = {中間解の保持を用いた量子アニーリングの精度向上手法},
 year = {2022}
}