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Lecture notes in computer science

Accelerating Simulated Quantum Annealing with GPU and Tensor Cores

Yi-Hua Chung, Cheng-Jhih Shih, Shih‐Hao Hung

Published Jan 1, 2022• 7 citations
Publisher
DOI
10.1007/978-3-031-07312-0_9

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We study large-scale applications using a GPU-accelerated version of the massively parallel Jülich universal quantum computer simulator (JUQCS–G). First, we benchmark JUWELS Booster, a GPU cluster with 3744 NVIDIA A100 Tensor Core GPUs. Then, we use JUQCS–G to study the relation between quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA). We find that a very coarsely discretized version of QA, termed approximate quantum annealing (AQA), performs surprisingly well in comparison to the QAOA. It can either be used to initialize the QAOA, or to avoid the costly optimization procedure altogether. Furthermore, we study the scaling of the success probability when using AQA for problems with 30 to 40 qubits. We find that the case with the largest discretization error scales most favorably, surpassing the best result obtained from the QAOA.
Simulated quantum annealing (SQA) is a probabilistic approximation method to find a solution for a combinatorial optimization problem using digital computers. The processing time of SQA increases exponentially with the number of variables. Therefore, acceleration of SQA is regarded as a very important topic. However, parallel implementation is difficult due to the serial nature of the quantum Monte Carlo algorithm used in SQA. In this paper, we propose a method to implement SQA in parallel on a GPU while preserving the data dependency. According to the experimental results, we have achieved over 97 times speed-up while maintaining the same accuracy-level compared to a single-core CPU implementation.