Reformulating bi-level MINLP transport vulnerability analysis into QUBO form allows D-Wave quantum annealing to solve disruption scenarios on networks up to 6018 links in minutes, one to two orders of magnitude faster than classical metaheuristics.
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arXiv preprint arXiv:1811.11538
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A hybrid quantum framework decomposes CVRP into bounded-width knapsack subproblems, trains a reinforcement learning controller for Lagrangian multipliers, and uses a contextual bandit to adapt quantum hardware execution, yielding improved routing quality on standard test instances.
Constraint-aware initialization and hybrid XY-X mixer in QAOA for VRP yield lower average energies and higher feasible-solution ratios than standard QAOA across ideal, finite-shot, and noisy simulations.
A hybrid classical-plus-quantum-inspired framework for cross-region renewable energy forecasting matches top baselines within 1% accuracy and separates calm versus stormy conditions with a 15-fold higher Fisher discriminant ratio than a tuned radial basis kernel.
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
BBQ-mIS decomposes graph coloring into parallel maximum independent set instances on Rydberg quantum hardware combined with classical branch-and-bound to produce proper colorings with few colors.
A unified local light-shifts encoding maps QUBO instances of SAT variants, set packing, quadratic assignment, clustering, and protein folding onto Rydberg annealers and solves them via optimized quantum annealing.
Impact-guided hybrid quantum decomposition for traffic zone partitioning improves convergence and spatial coherence over classical refinement but does not outperform direct quantum optimization on IBM hardware.
Hybrid quantum-classical solver using Benders decomposition and QUBO reduces crude oil scheduling costs by 73-80% versus metaheuristics on 15 test instances while matching commercial solver speed.
Iterative cutting-plane generation and arc preprocessing reduce TSP model size and yield performance gains on classical, direct quantum, and hybrid D-Wave solvers.
citing papers explorer
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Quantum Optimisation for Transport Vulnerability Identification
Reformulating bi-level MINLP transport vulnerability analysis into QUBO form allows D-Wave quantum annealing to solve disruption scenarios on networks up to 6018 links in minutes, one to two orders of magnitude faster than classical metaheuristics.
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Qubit-Scalable CVRP via Lagrangian Knapsack Decomposition and Noise-Aware Quantum Execution
A hybrid quantum framework decomposes CVRP into bounded-width knapsack subproblems, trains a reinforcement learning controller for Lagrangian multipliers, and uses a contextual bandit to adapt quantum hardware execution, yielding improved routing quality on standard test instances.
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Improving Feasibility in Quantum Approximate Optimization Algorithm for Vehicle Routing via Constraint-Aware Initialization and Hybrid XY-X Mixing
Constraint-aware initialization and hybrid XY-X mixer in QAOA for VRP yield lower average energies and higher feasible-solution ratios than standard QAOA across ideal, finite-shot, and noisy simulations.
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A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
A hybrid classical-plus-quantum-inspired framework for cross-region renewable energy forecasting matches top baselines within 1% accuracy and separates calm versus stormy conditions with a 15-fold higher Fisher discriminant ratio than a tuned radial basis kernel.
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Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
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BBQ-mIS: a parallel quantum algorithm for graph coloring problems
BBQ-mIS decomposes graph coloring into parallel maximum independent set instances on Rydberg quantum hardware combined with classical branch-and-bound to produce proper colorings with few colors.
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A Unified Local Light-shifts Encoding For Solving Optimization Problems on a Rydberg Annealer
A unified local light-shifts encoding maps QUBO instances of SAT variants, set packing, quadratic assignment, clustering, and protein folding onto Rydberg annealers and solves them via optimized quantum annealing.
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Impact-Driven Quantum Decomposition for Traffic Zone Partitioning: A Hybrid Gate-Model Framework
Impact-guided hybrid quantum decomposition for traffic zone partitioning improves convergence and spatial coherence over classical refinement but does not outperform direct quantum optimization on IBM hardware.
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Solve Crude Oil Scheduling Problems by Using Quantum-Classical Hybrid Algorithms
Hybrid quantum-classical solver using Benders decomposition and QUBO reduces crude oil scheduling costs by 73-80% versus metaheuristics on 15 test instances while matching commercial solver speed.
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Cutting-plane methodology via quantum optimization for solving the Traveling Salesman Problem
Iterative cutting-plane generation and arc preprocessing reduce TSP model size and yield performance gains on classical, direct quantum, and hybrid D-Wave solvers.