2020/07/06 by Erica Grant, Travis Humble, Travis S. Humble +1 · 77 citations
Computer Science · Physics and Astronomy · #Benchmark (surveying) #Benchmarking #Portfolio #Portfolio optimization #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum annealing #Simulated annealing #quant-ph
paper · pdf · doi:10.1103/physrevapplied.15.014012
published in Physical Review Applied 15(1) (American Physical Society) · 14 pages, 14 figures
arxiv created 2020/07/06 · openalex created_date 2020/07/10 · openalex publication_date 2021/01/08 · arxiv updated 2021/01/13 · openalex updated_date 2026/08/05
Quantum annealing offers an approach to finding the optimal solutions for a variety of computational problems, where the quantum annealing controls influence the observed performance and error mechanisms by tuning the underlying quantum dynamics. However, the influence of the available controls is often poorly understood, and methods for evaluating the effects of these controls are necessary to tune quantum computational performance. Here we use portfolio optimization as a case study by which to benchmark quantum annealing controls and their relative effects on computational accuracy. We compare empirical results from the D-Wave 2000QTM quantum annealer to the computational ground truth for a variety of portfolio optimization instances. We evaluate both forward and reverse annealing methods and we identify control variations that yield optimal performance in terms of probability of success and probability of chain breaks.