In this work we present a complete hybrid classical-quantum algorithm involving a quantum sampler based on neutral-atom platforms. This approach is inspired by classical column-generation frameworks developed in the field of operations research and shows how quantum procedures can assist classical solvers in addressing hard combinatorial problems. We benchmark our method on the minimum vertex coloring problem and show that the proposed hybrid quantum-classical column-generation algorithm can yield good solutions in relatively few iterations. We compare our results with state-of-the-art classical and quantum approaches.
In this work we present a complete hybrid classical-quantum algorithm involving a quantum sampler based on neutral-atom platforms. This approach is inspired by classical column-generation frameworks developed in the field of operations research and shows how quantum procedures can assist classical solvers in addressing hard combinatorial problems. We benchmark our method on the minimum vertex coloring problem and show that the proposed hybrid quantum-classical column-generation algorithm can yield good solutions in relatively few iterations. We compare our results with state-of-the-art classical and quantum approaches.
In this work, we develop new 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 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 post-processing technique, Make\_Diff, which applies bit-wise modifications to degenerate samples in order to return a non-degenerate 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 (SPAM) 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.
Efficient end-to-end entanglement distribution in quantum information networks requires routing under limited resources and fidelity constraints. We study entanglement routing as a fidelity-constrained unsplittable multicommodity flow problem that maximizes the number of admitted requests. As a proof of concept, we integrate neutral-atom quantum optimization into a hybrid classical--quantum column-generation framework. A classical restricted master problem selects routes, while a pricing problem generates fidelity-feasible paths. We formulate this NP-hard constrained shortest-path pricing problem as a quadratic unconstrained binary optimization (QUBO) problem and address it with hardware-aware register embedding and instance-driven pulse shaping. To our knowledge, this is the first study of neutral-atom quantum optimization as a pricing oracle for fidelity-constrained entanglement routing. On small benchmark instances evaluated with neutral-atom processor emulators, the method, combined with warm-start and post-processing, achieves an optimality gap below 1% across all tested sizes; the selected simulated-annealing route-generation baseline exhibits gaps of up to 6%. These results indicate that the generated bitstrings can provide useful candidate routes for classical refinement within column generation. This proof of concept does not establish quantum utility or scalability; further evaluation on larger instances, against stronger classical baselines, and on quantum hardware is needed.
Quantum and physics-inspired solvers provide specialized sampling interfaces for Ising and quadratic unconstrained binary optimization models, but constrained optimization requires broader coordination and certification. This survey uses classical decomposition to analyze how such solvers can be integrated into hybrid quantum-classical algorithms. We distinguish formal decomposition, reformulation and reduction, and computational orchestration. For each interface, we ask what the transformation preserves, which assumptions make the outer method converge, and which certificates the implemented inner routine supplies. The central questions are whether that information meets the classical algorithm's requirements and whether hybridization improves solution quality or total cost enough to justify its coordination overhead. Benders decomposition, column generation, graph reduction, and copositive methods illustrate this distinction by separating heuristic proposals from classical certification. We also distinguish component effects within a fixed wrapper, reduction--solver interactions, and comparisons between complete quantum-enabled and classical systems. The analyzed studies provide conditional component benefits and favorable co-design evidence, but do not establish transferable end-to-end superiority over strong classical comparators. The resulting framework identifies opportunities for constraint programming and operations research in structure-preserving design, safeguarded hybrid algorithms, and controlled benchmarking.
Neutral atom quantum computers (NAQCs) have emerged as a promising platform for solving the maximum weighted independent set (MWIS) problem. However, analog quantum approaches face two key limitations: constraints of the atomic layout on realizable graph geometries and the absence of performance guarantees. We introduce Lp-Quts, a hybrid quantum-classical framework that integrates an NAQC sampler into a classical cutting-plane algorithm. At each iteration, a relaxed linear program (RLP) bounds the MWIS and induces a reduced graph from which independent sets are sampled using an analog quantum sampler. A novel sample-informed separation problem guides odd-cycle cut selection and accelerates convergence. For t-perfect graphs, Lp-Quts inherits polynomial-time convergence guarantees from the classical theory of cutting planes. We evaluate our approach on instances with up to 300 vertices -- a scale that exceeds the capabilities of current NAQC hardware. In this regime, Lp-Quts reaches solutions within 5--10\% of optimality, outperforming direct analog quantum protocols and greedy baselines under equal sampling budgets. As expected, simulated annealing remains the strongest sample-based solver at this scale. These results demonstrate how quantum samplers can be effectively embedded within classical optimization frameworks to deliver near-optimal solutions with reduced quantum resources while preserving formal guarantees.
Quantum optimization provides a promising approach for solving large-scale combinatorial problems through quadratic unconstrained binary optimization (QUBO) formulations. However, integrating QUBO-based solvers into structured optimization frameworks while preserving solution guarantees remains a fundamental challenge. This paper develops a hybrid quantum-classical column-and-constraint generation (QCCG) framework for solving two-stage adaptive robust optimization problems with binary first-stage decisions and linear recourse under polyhedral uncertainty. The proposed approach reformulates the restricted master problem as a QUBO and solves it approximately using a quantum optimizer, while retaining a classical adversarial subproblem to compute worst-case recourse and certify solution quality. We construct a constraint-preserving QUBO encoding for inequality-constrained master problems using slack variables and penalty terms, enabling general mixed-integer structures to be mapped to quantum-compatible representations. To address inexactness arising from discretization, penalty modeling, and quantum optimization, we introduce a bound-adjustment mechanism that yields valid lower and upper bounds and provides a certified stopping criterion. We show that the proposed framework generalizes classical column-and-constraint generation and retains its convergence properties when the master problem is solved exactly. Numerical experiments on two-stage robust location-transportation problems demonstrate that the proposed hybrid approach achieves solution quality comparable to classical methods while reducing the computational burden associated with solving mixed-integer master problems, highlighting the potential of hybrid quantum-classical optimization for scalable decision-making under uncertainty.
This study explores the application of a hybrid quantum-classical algorithm for solving the parallel machine scheduling problem (PMSP) with sequence-dependent set-up times, a pivotal scheduling problem that has applications in multiple industries. Using a column generation-based approach, we propose a heuristic that combines a classical linear relaxation for the master problem with quantum annealing (QA) for solving the pricing subproblem (PSP). Whereas the PSP generates columns (i.e. a sequence of jobs that are assigned to a machine), the master problem selects which columns to use in order to minimize the makespan of the schedule. To generate columns, the PSP solves a travelling salesman problem (TSP) that is formulated as a quadratic unconstrained binary optimization (QUBO) problem. The big advantage thereof is that subtours can be eliminated by use of quadratic terms in the objective function. In addition, our approach also leverages the quantum annealer's capability to generate many high-quality solutions (i.e. columns) in a very short time. To assess the performance of our hybrid column generation-based heuristic, we perform a computational experiment. The results of this experiment demonstrate the synergy of hybrid methods for tackling complex decision-making problems, achieving competitive high-quality solutions and computational advantages when compared with classical solution methods. This article is part of the theme issue 'Quantum theory and topology in models of decision making (Part 2)'.