[Joint CQSE & NCTS Seminar] Benchmarking Quantum, Digital, and GPU Annealers

Title: [Joint CQSE & NCTS Seminar] Benchmarking Quantum, Digital, and GPU Annealers
Speaker: Prof. Jehn-Ruey Jiang (National Central University)
Time: 2024/5/3 (Fri.) 14:30-15:30
Place: NCTS Physics Lecture Hall, 4F, Chee-Chun Leung Cosmology Hall, NTU
Online: https://nationaltaiwanuniversity-zbh.my.webex.com/nationaltaiwanuniversity-zbh.my/j.php?MTID=mb00e764354d74a2b322c54de4cfdf7da
 

Abstract
Annealers leverage quadratic unconstrained binary optimization (QUBO) formulas
to address combinatorial optimization problems (COPs) and have shown potential to
outperform classical computers. This talk demonstrates benchmarking of three
prominent types of annealers, namely quantum, digital, and GPU annealers. The
quantum annealer (QA) is exemplified by the D-Wave Advantage, which relies on the
quantum tunneling mechanism to rapidly locate the minimum-energy system state
corresponding to the optimal solution to a COP. The digital annealer (DA) is typified by
the Fujitsu Digital Annealing Unit (DAU), which is based on a quantum-inspired digital
technology architecture to perform parallel and real-time optimization calculations to
solve a COP. The GPU annealer (GPUA) is exemplified by the Compal Quantix solver,
which harnesses graphics processing units (GPUs) to conduct adaptive bulk searches for
the optimal COP solution. This talk first provides introductory overviews of QA, DA,
and GPUA and then proceeds to benchmark their performance on various well-known
COPs such as the subset sum, maximum cut, vertex cover, 0/1 knapsack, graph
coloring, Hamiltonian cycle, traveling salesperson, and job shop scheduling problems.
Their performance is also compared with that of state-of-the-art algorithms running on
classical computers. This talk is concluded with an overall qualitative comparison of
QA, DA, and GPUA.
 
Biography
Prof. Jehn-Ruey Jiang received his Ph.D. degree in computer science in 1995 from
National Tsing Hua University, Hsinchu, Taiwan. He is currently with the department of
computer science and information engineering, National Central University, Taoyuan,
Taiwan. He is also leading the Advanced Computing And Networking (ACAN)
Laboratory, which focuses on investigating advanced technologies about computing and
networking. His research interests include quantum annealing algorithms, universal
quantum algorithms, quantum computing, quantum Internet, as well as machine
learning/deep learning and quantum machine learning/deep learning.

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