Towards solving industrial integer linear programs with Decoded Quantum Interferometry
Abstract
Optimization via decoded quantum interferometry (DQI) has recently gained a great deal of attention as a promising avenue for solving optimization problems using quantum computers. In this paper, we apply DQI to an industrial optimization problem in the automotive industry: the vehicle option-package pricing problem. Our main contributions are 1) formulating the industrial problem as an integer linear program (ILP), 2) converting the ILP into instances of max-XORSAT, and 3) developing a detailed quantum circuit implementation for belief propagation, a heuristic algorithm for decoding LDPC codes. Thus, we provide a full implementation of the DQI algorithm using Belief Propagation, which can be applied to any industrially relevant ILP by first transforming it into a max-XORSAT instance. We also evaluate the effectiveness of our implementation by benchmarking it against both Gurobi and a random sampling baseline.
Cite
@article{arxiv.2509.08328,
title = {Towards solving industrial integer linear programs with Decoded Quantum Interferometry},
author = {Francesc Sabater and Ouns El Harzli and Geert-Jan Besjes and Marvin Erdmann and Johannes Klepsch and Jonas Hiltrop and Jean-Francois Bobier and Yudong Cao and Carlos A. Riofrio},
journal= {arXiv preprint arXiv:2509.08328},
year = {2026}
}
Comments
47 pages, 16 figures. Code available at https://bcg-x-official.github.io/dqi/