Exploration of Quantum Machine Learning and AI Accelerators for Fusion Science

Technical Report · Fri Jan 14 04:00:00 EST 2022

DOI:https://doi.org/10.2172/1840522· OSTI ID:1840522

Liu, Minzhao ^[1]; Dong, Ge ^[2]; Felker, Kyle Gerard ^[3]; Otten, Matthew ^[4]; Balaprakash, Prasanna ^[3]; Tang, William ^[2]; Alexeev, Yuri ^[3]

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1. Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
2. Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
3. Argonne National Lab. (ANL), Argonne, IL (United States)
4. HRL Laboratories, LLC, Malibu, CA (United States)

The dawn of the noisy intermediate-scale quantum (NISQ) era sparked rapid development in variational quantum algorithms. These algorithms, utilizing parameterized quantum circuit optimized by classical computers with feedback, are practical under the constraints of the current hardware and can potentially show quantum advantage. In recent years, these variational circuit are applied to neural networks, hoping to boost the triumphant success of deep learning. We simulate various of quantum-classical hybrid neural networks applied to scientific tasks, and seek to understand their capabilities and limitations. Specifically, we devise quantum convolutional layers and apply them to deep neural networks used for predicting plasma disruption in fusion reactors.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1840522
• Report Number(s): ANL/CPS-21/3; 172142
• Country of Publication: United States
• Language: English

Related Subjects

70 PLASMA PHYSICS AND FUSION TECHNOLOGY
97 MATHEMATICS AND COMPUTING