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This paper proposes a QUBO-based method for selecting evidence passages in retrieval-augmented question answering, achieving competitive performance with LLM-based selectors while enabling the use of unconventional solvers like quantum annealers.
This paper presents a QUBO-based model for coordinating departure sequencing and track allocation in railway short-term concentrated departure scenarios, evaluated using simulation and hybrid quantum algorithms. Results show quantum-enhanced methods reduce cost and delay under dynamic conditions.
This paper proposes a quantum annealing approach that reformulates client selection in federated learning as a QUBO problem to defend against Byzantine attacks, showing improved detection accuracy over classical MultiKrum on sophisticated attacks, especially when combined with a MultiSignal ensemble.