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Entanglement as a resource in adiabatic quantum optimization

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arxiv 1501.06914 v1 pith:TE6MEA6P submitted 2015-01-27 cond-mat.dis-nn quant-ph

classification cond-mat.dis-nnquant-ph
keywords entanglementquantumevolutionadiabaticamountstateoptimizationstates
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We explore the role of entanglement in adiabatic quantum optimization by performing approximate simulations of the real-time evolution of a quantum system while limiting the amount of entanglement. To classically simulate the time evolution of the system with a limited amount of entanglement, we represent the quantum state using matrix-product states and projected entangled-pair states. We show that the probability of finding the ground state of an Ising spin glass on either a planar or non-planar two-dimensional graph increases rapidly as the amount of entanglement in the state is increased. Furthermore, we propose evolution in complex time as a way to improve simulated adiabatic evolution and mimic the effects of thermal cooling of the quantum annealer.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Variational matrix product states for combinatorial optimization

    quant-ph 2025-12 conditional novelty 6.0 of 10

    Quantum-inspired product/matrix-product-state annealing embedded in iterated local search reports better MaxCut approximations than the ILS, LQA, GCS, and QAOA baselines tested, on graphs up to 50,000 vertices.

  2. Entanglement Scaling and Problem Structure in Quantum Approximate and Adiabatic Optimization Algorithms

    quant-ph 2026-06 unverdicted novelty 5.0 of 10

    Empirical evidence indicates QAOA entanglement scales like fermionic Gaussian states for MaxCut instances, unlike the annealing-schedule-dependent scaling in adiabatic quantum computation.

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