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Entanglement as a resource in adiabatic quantum optimization
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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
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Variational matrix product states for combinatorial optimization
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.
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Entanglement Scaling and Problem Structure in Quantum Approximate and Adiabatic Optimization Algorithms
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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