TY - GEN
T1 - A Method for Reducing Time-to-Solution in Quantum Annealing Through Pausing
AU - Zielewski, Michael Ryan
AU - Takizawa, Hiroyuki
N1 - Funding Information:
This work is partially supported by MEXT Next Generation High-Performance Computing Infrastructures and Applications R&D Program “R&D of A Quantum-Annealing-Assisted Next Generation HPC Infrastructure and its Applications."
Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/1/7
Y1 - 2022/1/7
N2 - Recent research has shown that alternative annealing schedules provide the means for improving performance in modern quantum annealing devices. One such type of schedule is forward annealing with a pause, in which there is a period of time when system evolution is paused. While the results from using this type of schedule have been promising, effectively using a pause is not a trivial task. One challenge associated with introducing a pause into the schedule is determining the point in the anneal at which the pause will start. Additionally, tuning the schedule in real-time requires a significant amount of time. A second challenge is that while a pause may increase the number of correct solutions returned from the annealer, the time-to-solution, a standard metric for measuring performance in quantum annealing, will not necessarily be improved. We propose a method for constructing annealing schedules containing a pause that avoids the costly process of determining the optimal pause location in an online manner. We also evaluate our method on the subset sum problem, a problem of practical significance, and show that our method is able to achieve a 70% reduction in time-to-solution from a standard schedule containing no pause.
AB - Recent research has shown that alternative annealing schedules provide the means for improving performance in modern quantum annealing devices. One such type of schedule is forward annealing with a pause, in which there is a period of time when system evolution is paused. While the results from using this type of schedule have been promising, effectively using a pause is not a trivial task. One challenge associated with introducing a pause into the schedule is determining the point in the anneal at which the pause will start. Additionally, tuning the schedule in real-time requires a significant amount of time. A second challenge is that while a pause may increase the number of correct solutions returned from the annealer, the time-to-solution, a standard metric for measuring performance in quantum annealing, will not necessarily be improved. We propose a method for constructing annealing schedules containing a pause that avoids the costly process of determining the optimal pause location in an online manner. We also evaluate our method on the subset sum problem, a problem of practical significance, and show that our method is able to achieve a 70% reduction in time-to-solution from a standard schedule containing no pause.
KW - annealing schedule
KW - D-Wave
KW - pausing
KW - quantum annealing
UR - http://www.scopus.com/inward/record.url?scp=85122624748&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122624748&partnerID=8YFLogxK
U2 - 10.1145/3492805.3492815
DO - 10.1145/3492805.3492815
M3 - Conference contribution
AN - SCOPUS:85122624748
T3 - ACM International Conference Proceeding Series
SP - 137
EP - 145
BT - Proceedings of International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2022
PB - Association for Computing Machinery
T2 - 5th International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2022
Y2 - 12 January 2022 through 14 January 2022
ER -