2026-05-13T01:03:55Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00220431 2025-01-19T14:34:27Z 1164:10193:10905:11012

Latency-aware adaptive optimizer for real-time frugal variational quantum algorithms Latency-aware adaptive optimizer for real-time frugal variational quantum algorithms Kosuke, Ito Kosuke, Ito Variational quantum algorhtms (VQAs) are promising in near-term applications of quantum computers. For practical implementations of VQAs, efficient classical optimizers are actively being explored. Previous research proposed strategies to allocate the number of measurement shots per iteration of stochastic gradient descent by maximizing the gain per shot. Those strategies actually achieve fast convergence with respect to the total shot number. However, not only the shot number, we should take into account the circuit-switching latency as it is large compared to per-shot time in practice. In cloud services, per-task price is several orders of magnitude greater than per-shot price. In this work, we propose a strategy to adaptively allocate the appropriate shot number of each iteration to reduce the total real time or the cost for convergence by taking into account the latency time or per-task cost. We numerically show the efficacy of our algorithm. Variational quantum algorhtms (VQAs) are promising in near-term applications of quantum computers. For practical implementations of VQAs, efficient classical optimizers are actively being explored. Previous research proposed strategies to allocate the number of measurement shots per iteration of stochastic gradient descent by maximizing the gain per shot. Those strategies actually achieve fast convergence with respect to the total shot number. However, not only the shot number, we should take into account the circuit-switching latency as it is large compared to per-shot time in practice. In cloud services, per-task price is several orders of magnitude greater than per-shot price. In this work, we propose a strategy to adaptively allocate the appropriate shot number of each iteration to reduce the total real time or the cost for convergence by taking into account the latency time or per-task cost. We numerically show the efficacy of our algorithm. 情報処理学会 2022-10-20 eng technical report https://ipsj.ixsq.nii.ac.jp/records/220431 2435-6492 AA12894105 研究報告量子ソフトウェア（QS） 2022-QS-7 27 1 8 https://ipsj.ixsq.nii.ac.jp/record/220431/files/IPSJ-QS22007027.pdf application/pdf 1.3 MB 2024-10-20