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arXiv · 2305.02179

Quantum Inspired Optimization for Industrial Scale Problems

William P. Banner·Shima Bab Hadiashar·Grzegorz Mazur·Tim Menke·Marcin Ziolkowski·Ken Kennedy·Jhonathan Romero·Yudong Cao·Jeffrey A. Grover·William D. Oliver
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Abstract

Model-based optimization, in concert with conventional black-box methods, can quickly solve large-scale combinatorial problems. Recently, quantum-inspired modeling schemes based on tensor networks have been developed which have the potential to better identify and represent correlations in datasets. Here, we use a quantum-inspired model-based optimization method TN-GEO to assess the efficacy of these quantum-inspired methods when applied to realistic problems. In this case, the problem of interest is the optimization of a realistic assembly line based on BMW's currently utilized manufacturing schedule. Through a comparison of optimization techniques, we found that quantum-inspired model-based optimization, when combined with conventional black-box methods, can find lower-cost solutions in certain contexts.

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William P. Banner, Shima Bab Hadiashar, Grzegorz Mazur, Tim Menke, Marcin Ziolkowski, Ken Kennedy, Jhonathan Romero, Yudong Cao, Jeffrey A. Grover, William D. Oliver. 2023-05-03. Quantum Inspired Optimization for Industrial Scale Problems. https://arxiv.org/abs/2305.02179

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