2022
DOI: 10.1038/s41467-022-29887-0
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Analytical solution for nonadiabatic quantum annealing to arbitrary Ising spin Hamiltonian

Abstract: Ising spin Hamiltonians are often used to encode a computational problem in their ground states. Quantum Annealing (QA) computing searches for such a state by implementing a slow time-dependent evolution from an easy-to-prepare initial state to a low energy state of a target Ising Hamiltonian of quantum spins, HI. Here, we point to the existence of an analytical solution for such a problem for an arbitrary HI beyond the adiabatic limit for QA. This solution provides insights into the accuracy of nonadiabatic c… Show more

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Cited by 15 publications
(5 citation statements)
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“…On the other hand, the data loading is part of the problem formulation for QA, which is a time-consuming task for gate-based QC. However, there is no guaranteed speed-up or better solutions proofed for QA [54]. The use of hybrid solvers helps to improve the quality of solutions and problem size.…”
Section: Discussionmentioning
confidence: 99%
“…On the other hand, the data loading is part of the problem formulation for QA, which is a time-consuming task for gate-based QC. However, there is no guaranteed speed-up or better solutions proofed for QA [54]. The use of hybrid solvers helps to improve the quality of solutions and problem size.…”
Section: Discussionmentioning
confidence: 99%
“…As the energy gap between the global minimum and the adjacent higher state diminishes linearly, the required annealing time for successful adiabaticity grows exponentially [4,27], surpassing the limits of contemporary QAs [99]. Nonetheless, QAs, akin to other QCs, are advancing; subsequent generations are expected to bypass present technological constraints.…”
Section: Anneal Time: Current Technological Barriersmentioning
confidence: 99%
“…Due to this specific structure, so far, materials science related applications of this technology are still rare. Instead, actual research focuses mainly on the benchmarking and performance tests of quantum annealing compared to classical approaches [12][13][14] . Some first applications in the field of biology and traffic research in the sense of optimization problems have been developed recently.…”
Section: Introductionmentioning
confidence: 99%