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arXiv · 2509.08328

Towards solving industrial integer linear programs with Decoded Quantum Interferometry

Francesc Sabater·Ouns El Harzli·Geert-Jan Besjes·Marvin Erdmann·Johannes Klepsch·Jonas Hiltrop·Jean-Francois Bobier·Yudong Cao·Carlos A. Riofrio
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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.

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Francesc Sabater, Ouns El Harzli, Geert-Jan Besjes, Marvin Erdmann, Johannes Klepsch, Jonas Hiltrop, Jean-Francois Bobier, Yudong Cao, Carlos A. Riofrio. 2025-09-10. Towards solving industrial integer linear programs with Decoded Quantum Interferometry. https://doi.org/10.1088/2058-9565%2Fae65d8

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