Quantum Inspired Optimization for Industrial Scale Problems

• URL: http://arxiv.org/abs/2305.02179v1
• Date: Wed, 3 May 2023 15:19:36 GMT
• Title: Quantum Inspired Optimization for Industrial Scale Problems
• Authors: William P. Banner, Shima Bab Hadiashar, Grzegorz Mazur, Tim Menke, Marcin Ziolkowski, Ken Kennedy, Jhonathan Romero, Yudong Cao, Jeffrey A. Grover, William D. Oliver
• Abstract summary: 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.
• Score: 0.5417521241272644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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