ILOD approximates X-Z error correlations in Ising-based quantum decoding via iterative Bayesian reweighting, halving maximum interaction order and restoring convergence at larger code distances.
Dattani, Quadratization in discrete optimization and quantum mechanics, arxiv:1901.04405 (2019)
7 Pith papers cite this work. Polarity classification is still indexing.
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DQI-Kit automates encoding of objectives and constraints into Max-LINSAT instances and estimates expected DQI performance on the resulting problems.
BoolXLLM augments an existing Boolean rule learner with LLMs for feature selection, discretization thresholds, and natural-language rule translation to improve interpretability while preserving accuracy.
kA-QAOA matches MA-QAOA approximation ratios on 3-uniform hypergraphs while using significantly fewer function evaluations.
End-to-end runtime definitions and strong classical baselines show that three recent quantum advantage claims in annealing, Simon's problem, and hybrid algorithms do not hold on NISQ hardware.
VeloxQ is a classical QUBO solver that reports competitive or superior performance and unique scalability to 10^8-variable sparse instances across benchmarks against quantum annealers, physics-inspired methods, and conventional solvers.
Quantum annealing is described as a heuristic for discrete optimization and sampling that also serves as a platform for studying non-equilibrium many-body quantum dynamics with programmable spin systems.
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Recent quantum runtime (dis)advantages
End-to-end runtime definitions and strong classical baselines show that three recent quantum advantage claims in annealing, Simon's problem, and hybrid algorithms do not hold on NISQ hardware.
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VeloxQ: A Fast and Efficient QUBO Solver
VeloxQ is a classical QUBO solver that reports competitive or superior performance and unique scalability to 10^8-variable sparse instances across benchmarks against quantum annealers, physics-inspired methods, and conventional solvers.