A Feasibility Study for Quantum Computing Methodologies in Automotive Advanced Material Investigation Application for Functional Carbon Material Screening Problem with Quantum Inspired Methodologies
量子コンピューティングの自動車材料デザインへの適用可能性 カーボン材料探索への量子インスパイアード手法の適用
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- Publication code
- 20244237
- Paper/Info type
- JSAE Transaction
Vol.55 No.3
- Pages
- 621-627(Total 7 p)
- Date of publication
- May 2024
- Publisher
- JSAE
- Language
- Japanese
Detailed Information
| Category(J) | 論文 Translation |
|---|---|
| Category(E) | Paper |
| Author(J) | 1) 菅 義訓, 2) 丸尾 昭人, 3) 實宝 秀幸 |
| Author(E) | 1) Yoshinori Suga, 2) Akito Maruo, 3) Hideyuki Jippo |
| Affiliation(J) | 1) トヨタ自動車, 2) 富士通, 3) 富士通 |
| Abstract(J) | 量子コンピューティング技術の将来的な自動車材料デザインへの適用可能性に関し，量子インスパイアド手法の一つであるデジタルアニーリング（疑似量子アニーリング）を材料探索に適用した事例を紹介する． Translation |
| Abstract(E) | Recently, as the upcoming limitation of operational speed enhancement in conventional silicon device technologies, quantum computing methodologies are realized one of the promising candidates that could potentially instead conventional multi-core supercomputer platforms with GPUs. The functional material optimization problem, that idealize high performance electric vehicle powertrain systems, would be a suitable target for benchmarking the efficiency of this novel high performance computing technologies. Regarding this direction, for the purpose of elucidating latent powerfulness of quantum annealing algorithms in functional material design, we performed quantum inspired study for the optimization of compound compositional design with substituting heterogeneous elements. (Quantum inspired computing is as the simulation technology for solving specific Ising Hamiltonian that reproduce energetic behavior of systems under the latest GPU platforms.) Carbon material design, in relation to fuel cell catalyst or electrode material for Li ion battery in automotive application, was studied as the representative case in functional material investigation. Notable optimization performance improvement was observed. |
