3-regular three-XORSAT planted solutions benchmark of classical and quantum heuristic optimizers

Journal Article · 13 February 2022 · Quantum Science and Technology

DOI: https://doi.org/10.1088/2058-9565/ac4d1b · OSTI ID:1979466

Kowalsky, Matthew ^[1]; Albash, Tameem  ^[2]; Hen, Itay  ^[3]; Lidar, Daniel A.  ^[4]

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1. University of Southern California, Los Angeles, CA (United States); USRA Research Institute for Advanced Computer Science, Mountain View, CA (United States); NASA Ames Research Center, Moffett Field, CA (United States); OSTI
2. University of New Mexico, Albuquerque, NM (United States)
3. University of Southern California, Los Angeles, and Marina del Rey, CA (United States)
4. University of Southern California, Los Angeles, CA (United States)

With current semiconductor technology reaching its physical limits, special-purpose hardware has emerged as an option to tackle specific computing-intensive challenges. Optimization in the form of solving quadratic unconstrained binary optimization problems, or equivalently Ising spin glasses, has been the focus of several new dedicated hardware platforms. These platforms come in many different flavors, from highly-efficient hardware implementations on digital-logic of established algorithms to proposals of analog hardware implementing new algorithms. In this work, we use a mapping of a specific class of linear equations whose solutions can be found efficiently, to a hard constraint satisfaction problem (three-regular three-XORSAT, or an Ising spin glass) with a 'golf-course' shaped energy landscape, to benchmark several of these different approaches. We perform a scaling and prefactor analysis of the performance of Fujitsu's digital annealer unit (DAU), the D-Wave advantage quantum annealer, a virtual MemComputing machine, Toshiba's simulated bifurcation machine (SBM), the SATonGPU algorithm from Bernaschi et al, and our implementation of parallel tempering. We identify the SATonGPU and DAU as currently having the smallest scaling exponent for this benchmark, with SATonGPU having a small scaling advantage and in addition having by far the smallest prefactor thanks to its use of massive parallelism. Furthermore, our work provides an objective assessment and a snapshot of the promise and limitations of dedicated optimization hardware relative to a particular class of optimization problems.

• Research Organization: California Institute of Technology (CalTech), Pasadena, CA (United States)
• Sponsoring Organization: US Army Research Office; USDOE Office of Science (SC), High Energy Physics (HEP)
• Grant/Contract Number: SC0019219
• OSTI ID: 1979466
• Journal Information: Quantum Science and Technology, Journal Name: Quantum Science and Technology Journal Issue: 2 Vol. 7; ISSN 2058-9565
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: United States
• Language: English