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Learning-Driven Annealing with Adaptive Hamiltonian Modification for Solving Large-Scale Problems on Quantum Devices

We present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as short annealing times and integrated control errors. Unlike other iterative methods, LDA does not tune the annealing pro...

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Bibliografski detalji
Glavni autori: Sebastian Schulz, Dennis Willsch, Kristel Michielsen
Format: Artigo
Jezik:Inglês
Izdano: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften 2025-10-01
Serija:Quantum
Online pristup:https://quantum-journal.org/papers/q-2025-10-29-1898/pdf/
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