arXiv subjects

Wesley Coelho

Publications and source records attributed to Wesley Coelho.

5 recordsLinked to original sources

arXiv2026

Satellite Mission Planning with Rydberg Atoms

Quantum computers relying on cold atoms are being built and promise a high flexibility in the way information in encoded into the physical system. In particular, the analog mode is spiking interest in the field of optimization as a classically intractable number of configurations can be tackled. In this work, we investigate a problem that requires every-day scheduling of critical tasks involving a large number of actors. Namely, fixing the planning for a Earth Observation satellite fleet composed of several of units exposed to a high density of targets to be scanned. We explore numerical schemes that convert the formulated problem into a cold-atoms friendly setup. We begin by a naive formulation of the Satellite Mission Planning problem without taking account for the agility of the satellites. We then extend the problem to take it into account based on the literature. By formulating the planning problem as a Maximum Independent Set problem, we are able to solve the problem with a QPU based on Rydberg atoms. We explore two ways of solving the MIS problem on the QPU, one relying on the graphs and on the Quadratic Unconstrained Binary Optimization Framework (QUBO). We show that the QUBO methodology is the most relevant and explore it more deeply with numerical experiments. We conclude on the potential utility of using a QPU to solve the Satellite Mission Planning problem in an operational context.

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arXiv2025

Hybrid Quantum-Classical Branch-and-Price Method for the Vertex Coloring Problem

This paper introduces Quantum Classical Branch-and-Price (QCBP), a hybrid quantum-classical algorithm for the Vertex Coloring problem on neutral-atom Quantum Processing Units (QPUs). QCBP embeds quantum computation within the classical Branch-and-Price (BP) framework to address three bottlenecks in classical BP algorithms: the computational cost of Pricing Subproblems (PSPs), branching efficiency, and the quality of primal heuristics. It uses quantum-assisted Column Generation (CG) based on Quantum Adiabatic Algorithms (QAA) to sample high-quality maximum-weight independent sets (MWIS), reducing the need to repeatedly solve NP-hard PSPs. The adapted branching strategy leverages quantum-generated independent sets to explore fewer nodes, tighten lower bounds, and converge faster. A classical primal heuristic rapidly builds feasible solutions from quantum-generated sets, avoiding unnecessary quantum calls or additional Integer Linear Programming (ILP) solves. Compared with our prior Hybrid Column Generation (HCG) and Branch-and-Bound through maximal Independent Set (BBQ-mIS), QCBP improves both quantum-resource utilization and solution quality. Extensive experiments show QCBP significantly outperforms HCG and BBQ-mIS, reaching optimality on $\approx 98\%$ of benchmark instances. Preliminary validation on real neutral-atom hardware indicates robustness to quantum noise and hardware constraints, supporting practical applicability and scalability to larger graph instances. QCBP emerges as a viable hybrid method for combinatorial optimization with promising scalability on near-term quantum hardware.

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arXiv2025

A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors

Molecular docking is a critical computational method in drug discovery used to predict the binding conformation and orientation of a ligand within a protein's binding site. Mapping this challenge onto a graph-based problem, specifically the Maximum Weighted Independent Set (MWIS) problem, allows it to be addressed by specialized hardware such as neutral-atom quantum processors. However, a significant bottleneck has been the size mismatch between biologically relevant molecular systems and the limited capacity of near-term quantum devices. In this work, we overcome this scaling limitation by the use of a divide-and-conquer heuristic introduced in Cazals 2025. This algorithm decomposes a single, intractable graph instance into smaller sub-problems that can be solved sequentially on a neutral-atom quantum emulator, incurring only a linear computational overhead. We benchmark this approach on 10 real-world protein-ligand complexes, including 9 from the Astex Diverse Set, with graphs ranging from 225 to 585 vertices. The quantum heuristic consistently outperforms a greedy baseline and achieves the provably optimal solution on a 540-node instance (TACE-AS). We further assess the biological relevance of the reconstructed poses via the fraction of native contacts, and benchmark the full workflow on a standard dataset of diverse protein-ligand complexes. Our work establishes a scalable blueprint for applying quantum optimization to molecular docking, while identifying concrete directions for improving both the algorithmic strategy and the underlying graph model.

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arXiv2025

Local Rydberg blockade regimes for disk graph embedding and quantum optimization

Rydberg atom arrays are a powerful platform for solving combinatorial optimization problems, owing to the Rydberg blockade mechanism, which imposes effective constraints on simultaneous atomic excitations. These constraints have enabled the encoding of the Maximum Independent Set (MIS) problem on unit disk graphs, where atoms interact within a fixed, globally defined blockade radius. However, this restriction limits the class of addressable problems. A natural extension is to consider disk graphs, which generalize unit disk graphs by allowing arbitrary disk radii and correspond to the intersection graphs of disks in the plane. Embedding such graphs in Rydberg systems requires moving beyond the standard, globally uniform blockade model. In this work, we introduce a local Rydberg blockade regime, which emerges when local drives are applied to different pairs of atoms involved in a potential interaction. We develop a general theoretical framework for this regime and propose two novel metrics, the correlation matrix and the maximum independence violation, to quantify the quality of the embedding. Using these metrics, we demonstrate that disk graphs can be meaningfully embedded into Rydberg atom arrays under local drive schemes, thereby expanding the landscape of quantum-addressable optimization problems. Finally, when evaluating approximate solutions of the MIS problem, characterized by near-optimal independent sets, local drive approaches exhibit significantly improved performance over global ones. These results highlight the practical advantage of local blockade engineering for approximate combinatorial optimization and open a path toward leveraging the analog capabilities of Rydberg platforms beyond conventional geometric constraints.

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arXiv2025

Enhancing the Performance of Quantum Neutral-Atom-Assisted Benders Decomposition

This paper presents key enhancements to our previous work~\cite{naghmouchi2024mixed} on a hybrid Benders decomposition (HBD) framework for solving mixed integer linear programs (MILPs). In our approach, the master problem is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) model and solved on a neutral-atom quantum processor using automated conversion techniques. Our enhancements address three critical challenges. First, to adapt to hardware constraints, we refine the QUBO formulation by tightening the bounds of continuous variables and employing an exponential encoding method that eliminates slack variables, thereby reducing the required qubit count. Second, to improve solution quality, we propose a robust feasibility cut generation method inspired by the L-shaped approach and implement a constructive penalty tuning mechanism that replaces manual settings. Third, to accelerate convergence, we introduce a multi-cut strategy that integrates multiple high-density Benders cuts per iteration. Extensive numerical results demonstrate significant improvements compared to our previous approach: the feasibility rate increases from 68 percent to 100 percent, and the optimality rate rises from 52 percent to 86 percent . These advancements provide a solid foundation for future hybrid quantum-classical optimization solvers.

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