Enhancing Optimization Techniques with Quantum-Inspired Generative Models (Part 2)

ORAL

Abstract

Optimization with classical, quantum-inspired, or quantum methods often displays best performance when a high degree of problem knowledge is incorporated. In typical industrial applications this domain-specific knowledge already exists. In this work we identify a method of data encoding and search space reduction in a BMW plant optimization problem. The approach is based on a problem relaxation that incorporates varying degrees of problem knowledge. We then use this method to improve upon previous no-knowledge results. In particular, we show how problem knowledge helps the quantum-inspired optimizer find better solutions.

*This work is funded by the MIT Center for Quantum Engineering

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Presenters

  • Shima Bab Hadiashar

    • Zapata Computing Inc.

Authors

  • Shima Bab Hadiashar

    • Zapata Computing Inc.

  • William P Banner

    • Massachusetts Institute of Technology MIT

  • Grzegorz Mazur

    • Department of Computational Methods in Chemistry, Jagiellonian University
    • Zapata Computing Inc.

  • Tim Menke

    • Atlantic Quantum Corporation

  • Marcin Ziolkowski

    • BMW Group Information Technology Research Center

  • Jeffrey A Grover

    • Massachusetts Institute of Technology MIT
    • Massachusetts Institute of Technology (MIT)
    • Massachusetts Institute of Technology

  • Jhonathan Romero

    • Zapata Computing Inc

  • William D Oliver

    • Massachusetts Institute of Technology MIT
    • Massachusetts Institute of Technology (MIT), MIT Lincoln Laboratory
    • Massachusetts Institute of Technology (MIT)
    • Massachusetts Institute of Technology
    • Massachusetts Institute of Technology, MIT Lincoln Laboratory