To solve an optimization problem using a commercial quantum annealer, one has to represent the problem of interest as an Ising or a quadratic unconstrained binary optimization (QUBO) problem and submit its coefficients to the annealer, which then returns a user-specified number of low-energy solutions. It would be useful to know what happens in the quantum processor during the anneal process so that one could design better algorithms or suggest improvements to the hardware. However, existing quantum annealers are not able to directly extract such information from the processor. Hence, in this work we propose to use advanced features of D-Wave 2000Q to indirectly infer information about the dynamics of the state evolution during the anneal process. Specifically, D-Wave 2000Q allows the user to customize the anneal schedule, that is, the schedule with which the anneal fraction is changed from the start to the end of the anneal. Using this feature, we design a set of modified anneal schedules whose outputs can be used to generate information about the states of the system at user-defined time points during a standard anneal. With this process, called "slicing", we obtain approximate distributions of lowest-energy anneal solutions as the anneal time evolves. We use our technique to obtain a variety of insights into the annealer, such as the state evolution during annealing, when individual bits in an evolving solution flip during the anneal process and when they stabilize, and we introduce a technique to estimate the freeze-out point of both the system as well as of individual qubits.
Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, University of Science and Technology of China（中国科学技术大学物理科学学院、微观尺度物理国家研究中心）；Hefei National Laboratory, University of Science and Technology of China（中国科学技术大学合肥国家实验室）；Shanghai Research Center for Quantum Science and CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China（中国科学技术大学上海量子科学研究中心、中国科学院量子信息与量子物理学前沿卓越创新中心）
Utilizing intermediate states in quantum annealing for multi-objective optimization
Graduate School of Science and Technology, Keio University（庆应义塾大学理工学研究科）；Department of Applied Physics and Physico-Informatics, Keio University（庆应义塾大学应用物理及理化信息学系）；Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC), Keio University（庆应义塾大学可持续量子人工智能中心）；Human Biology-Microbiome-Quantum Research Center (WPI-Bio2Q), Keio University（庆应义塾大学生物人类学-微生物组-量子研究中心）；Green Computing System Research Organization, Waseda University（早稻田大学绿色计算系统研究组织）
Improving quantum annealing of the ferromagnetic $ p $-spin model through pausing
量子退火的成功概率可以通过在其动态过程中暂停退火机来显著提高，以受控的方式利用热弛豫。本文研究了全连接铁磁$ p $ -自旋模型中量子退火的暂停效应。我们用数值方法表明：（i）最佳暂停点比所分析实例的避免交叉时间长60%，以及（ii）在最佳暂停点，我们记录了45%的成功概率，相对于没有相同持续时间暂停的量子退火。这些结果与实验观察到的可用量子退火的连接较少的模型。观察到的改善$ p $ -自旋模型可以达到两个数量级相对于一个孤立的量子动力学相同的持续时间。
Frustration-enhanced quantum annealing correction models with additional inter-replica interactions
Graduate School of Science and Technology, Keio University（庆应义塾大学理工学部）；Department of Applied Physics and Physico-Informatics, Keio University（庆应义塾大学应用物理与物理信息学系）；Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC), Keio University（庆应义塾大学可持续量子人工智能中心）；Human Biology-Microbiome-Quantum Research Center (WPI-Bio2Q), Keio University（庆应义塾大学人类生物学-微生物组-量子研究中心）
Association between Prefrontal fNIRS signals during Cognitive tasks and College scholastic ability test (CSAT) scores: Analysis using a quantum annealing approach
Worldline-Susceptibility Scheduling for Quantum Annealing Beyond Local-Adiabatic Evolution
The Institute of Mathematical Sciences (IMSc)（数学科学研究所）；Pecslab Research（Pecslab研究）；Department of Physics, Ramjas College, University of Delhi（德里大学拉姆贾斯学院物理系）；Homi Bhabha National Institute（霍米·巴巴国立研究所）
