Lukas Bödeker
Автор: QuICS
Загружено: 2025-09-04
Просмотров: 74
Scalable error correction strategies and the memory capacity of open quantum neural networks
Due to the fragility of quantum states, quantum error correction is a necessary ingredient for scalable, beneficial quantum computation. In order not to lose the corrective power of an error correcting code, logical operations as well as the decoding need to be performed in a fault-tolerant way. In this context, I will present recent results on the modelling of lattice surgery for performing a logical state teleportation between surface codes, as well as recent experimental demonstration of fault-tolerant lattice surgery with quantum repetition codes [1]. Furthermore, I will outline how the question of finding a good decoder that determines the correction in an error corrected protocol can be answered by an interpretable machine learning ansatz [2]. In a second part, I will introduce a complementary concept for robustly storing information, which is that of associative networks such as the celebrated Hopfield network. Quantum generalizations of the Hopfield model allow for information storage in an open quantum system. We discuss and answer the question of how the asymptotic maximal memory capacity of such models can be evaluated [3].
[1] ArXiv: 2501.04612 (2025)
[2] Adv Quantum Technol., e2500158. (2025)
[3] Phys. Rev. Research 5, 023074. (2023)
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