2023 Fiscal Year Final Research Report
Custom Accelerators for Quantum-Annealing-Assisted Material Informatics
| Project/Area Number |
20H04197
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 60100:Computational science-related
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| Research Institution | Tohoku University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
Waidyasooriya Ha 東北大学, 情報科学研究科, 准教授 (60723533)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | FPGA / ヘテロジニアスコンピューティング / 高性能計算 / 量子化学 |
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| Outline of Final Research Achievements |
We developed an architecture for a quantum annealing simulator using FPGA for molecular structure optimization, and revealed that this acceleration method can also be applied to GPUs. Furthermore, by using a sparse Ising model, we reduced the amount of computation and created an architecture that can handle high parallel processing. In addition, we devised an architecture that divides quantum bits using multiple FPGAs, and demonstrated that the problem size can be scaled up. These studies have been shown to be for molecular structure optimization methods. Moreover, we developed a heterogeneous accelerator for large-scale quantum chemistry simulators combining FPGAs, CPUs, and GPUs, achieving up to 100 times faster speed compared to conventional methods.
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| Free Research Field |
高性能計算
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の学術的は、分子構造最適化のための量子アニーリングシミュレータのアーキテクチャとその高速化手法に関する新たな知見を提供した点にある．FPGA、CPU、GPUを組み合わせたヘテロジニアスアクセラレータの開発は、量子化学シミュレーションのパフォーマンスを大幅に向上させ、大規模なシミュレーションが必要な産業での時間とコストの削減が期待できる。さらに，社会的には，この新手法は薬物設計や新素材開発など様々な分野で応用可能で、大規模シミュレーションが必要な産業における効率化に貢献することが期待できる。
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