May 4, 2023

Quantum-Inspired Optimization for Industrial Scale Problems

  • Shima Bab Hadiashar
  • Jhonathan Romero Fontalvo
  • Yudong Cao
External Co-Authors:
  • William P. Banner – Massachusetts Institute of Technology
  • Grzegorz Mazur – Jagiellonian University
  • Tim Menke – Massachusetts Institute of Technology, Harvard University
  • Marcin Ziolkowski – BMW Group Information Technology Research Center
  • Ken Kennedy – BMW Group Information Technology Research Center
  • Jeffrey A. Grover – Massachusetts Institute of Technology
  • William D. Oliver – Massachusetts Institute of Technology


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.

Shima Bab Hadiashar
Zapata Author

Shima Bab Hadiashar , Ph.D.

Quantum Application Scientist, Professional Services
Jhonathan Romero Fontalvo
Zapata Author

Jhonathan Romero Fontalvo , Ph.D.

Director of Professional Services & Co-Founder
Yudong Cao
Zapata Author

Yudong Cao , Ph.D.

Chief Technology Officer & Co-Founder