#quantum optimization

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6 papers match

math.OC2026

Entropy-Smooth Convex Optimization Cannot Be Accelerated

Jacob M. Aguirre, Dmitrii M. Ostrovskii

The paper proves that for convex functions that are smooth relative to negative entropy (or von Neumann entropy in the quantum case), no first-order method can achieve an accelerat…

#convex optimization#entropy-smoothness#mirror descent#lower bounds
quant-ph2026

Principles of Quantum Optimization for Constrained Problems

Einar Gabbassov, Gurpahul Singh, Achim Kempf

The paper develops a spectral framework that links entanglement restructuring to computational slowdown in quantum algorithms for constrained combinatorial optimization, and shows…

#quantum optimization#constrained combinatorial problems#entanglement dynamics#spectral theory
quant-ph2026

Separating Geometry From Interference in Constrained Quantum Optimization

Chinonso Onah, Stuart Hadfield, Kristel Michielsen

The paper analyzes how constraint‑preserving mixing operators move quantum amplitudes in constrained optimization problems and shows that quantum advantage depends on aligning the…

#quantum optimization#constraint handling#mixing operators#quantum interference
quant-ph2026

Emulating XX catalysts for quantum annealing via self-consistent transverse fields

Mohammadhossein Dadgar, Christopher L. Baldwin

The paper proposes a method to emulate fully‑connected transverse interaction catalysts for quantum annealing using a self‑consistent transverse field that only requires measuremen…

#quantum annealing#transverse-field catalysts#self-consistent fields#p-spin model
cs.AR2026

A Reality Check on Quantum Optimisation: Evidence from an Industrial Case Study

Hila Safi, Karen Wintersperger, Oliver von Sicard +2

The paper evaluates quantum, quantum-inspired, and classical methods for solving an industrial job‑shop scheduling problem, comparing IBM Quantum, D‑Wave, and Fujitsu Digital Annea…

#quantum optimization#job-shop scheduling#quantum annealing#digital annealer
quant-ph2026

AutoQResearch: LLM-Guided Closed-Loop Policy Search for Adaptive Variational Quantum Optimization

Monit Sharma, Hoong Chuin Lau

The paper introduces AutoQResearch, a framework that uses large language models to autonomously search for adaptive policies that configure variational quantum algorithms for combi…

#variational quantum algorithms#quantum optimization#large language models#policy search