Ising machines outperform every tested Potts machine on Max-k-Cut problems, with the performance gap widening from k=3 to k=4.
Ising formulations of many NP problems
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Exhaustively parametrised feasibility-respecting quantum circuits can reach every feasible solution to problems like TSP with certainty using fixed parameters by leveraging group actions and generating sequences.
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.
Hybrid sector and path-window drivers achieve approximately 0.98 fidelity in centered barrier instances on hypercubes, outperforming standard transverse-field annealing for specific target Hamiltonians.
Simulations predict that a virtually connected photonic probabilistic computer solves Erdos-Renyi graph spin-glass ground states orders of magnitude faster than digital annealing units by avoiding embedding and sparsification.
GPU-based quantum-annealing-inspired algorithms outperform both quantum processors and industry classical solvers in sampling speed and full runtime on MO-MaxCut instances.
citing papers explorer
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Comparative Study of Potts Machine Dynamics and Performance for Max-k-Cut
Ising machines outperform every tested Potts machine on Max-k-Cut problems, with the performance gap widening from k=3 to k=4.
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Exhaustive and feasible parametrisation with applications to the travelling salesperson problem
Exhaustively parametrised feasibility-respecting quantum circuits can reach every feasible solution to problems like TSP with certainty using fixed parameters by leveraging group actions and generating sequences.
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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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Sector-dominant graph-local drivers for path-window barrier Hamiltonians on the Boolean hypercube
Hybrid sector and path-window drivers achieve approximately 0.98 fidelity in centered barrier instances on hypercubes, outperforming standard transverse-field annealing for specific target Hamiltonians.
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A virtually connected probabilistic computer as a solver for higher-order, densely connected, or reconfigurable combinatorial optimisation problems
Simulations predict that a virtually connected photonic probabilistic computer solves Erdos-Renyi graph spin-glass ground states orders of magnitude faster than digital annealing units by avoiding embedding and sparsification.
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Multi-Objective Optimization by Quantum-Annealing-Inspired Algorithms
GPU-based quantum-annealing-inspired algorithms outperform both quantum processors and industry classical solvers in sampling speed and full runtime on MO-MaxCut instances.