2015/03/03 by Alejandro Perdomo-Ortiz, Alejandro Perdomo‐Ortiz, Joseph Fluegemann +7 · 1 citation
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Advanced Bandit Algorithms Research #Advancements in Semiconductor Devices and Circuit Design #Computability, Logic, AI Algorithms #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Stochastic Gradient Optimization Techniques #quant-ph
paper · pdf · doi:10.48550/arxiv.1503.01083
10 pages
arxiv created 2015/03/03 · openalex publication_date 2015/03/03 · arxiv updated 2015/03/04 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
With the advent of large-scale quantum annealing devices, several challenges have emerged. For example, it has been shown that the performance of a device can be significantly affected by several degrees of freedom when programming the device; a common example being gauge selection. To date, no experimentally-tested strategy exists to select the best programming specifications. We developed a score function that can be calculated from a number of readouts much smaller than the number of readouts required to find the desired solution. We show how this performance estimator can be used to guide, for example, the selection of the optimal gauges out of a pool of random gauge candidates and how to select the values of parameters for which we have no a priori knowledge of the optimal value. For the latter, we illustrate the concept by applying the score function to set the strength of the parameter intended to enforce the embedding of the logical graph into the hardware architecture, a challenge frequently encountered in the implementation of real-world problem instances. Since the harder the problem instances, the more useful the strategies proposed in this work are, we expect the programming strategies proposed to significantly reduce the time of future benchmark studies and in help finding the solution of hard-to-solve real-world applications implemented in the next generation of quantum annealing devices.