Leveraging analog neutral-atom quantum computers for diversified pricing in hybrid column-generation frameworks
Phys. Rev. A 113, 022617 – Published 19 February, 2026
DOI: https://doi.org/10.1103/9bsf-gb5v
Abstract
In this work, we develop pulse designs and embedding strategies to improve the analog quantum subroutines of hybrid column generation (CG) algorithms based on neutral-atoms quantum computers (NAQCs). These strategies are designed to improve the quality and diversity of the samples generated. We apply these to an important combinatorial optimization (CO) problem in logistics, namely the fleet assignment. Depending on the instance tested, our quantum protocol has a performance that is either comparable to or worse than the best classical method tested, both in terms of the number of iterations and final objective value. We identify the cause of these suboptimal solutions as a result of our quantum protocol often generating high-quality but degenerate samples. We address this limitation by introducing a greedy postprocessing technique, Make_Diff, which applies bitwise modifications to degenerate samples in order to return a nondegenerate set. With this modification, our quantum protocol becomes competitive with an exact solver for the subproblem, all the while being resilient to state preparation and measurements errors. We also compare our CG scheme with a Gurobi solver and find that it performs better on over 50% of our synthetic instances and that, despite Gurobi having a more extensive runtime. These improvements and benchmarks herald the potential of deploying hybrid CG schemes on NISQ devices for industrially relevant CO problems.
