Self-Induced Glassy Phase in Multimodal Cavity Quantum Electrodynamics

Erba, Vittorio; Pastore, Mauro; Rotondo, Pietro

doi:10.1103/physrevlett.126.183601

Cited by 4 publications

(1 citation statement)

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“…Notably, reference [68] pointed out a practical protocol to implement associative memories, obtaining the specific form of these interactions for confocal cavities. The comprehension of the physics arising by such an effective interaction, although challenging [69], is fundamental for endowing theoretical proposals of open quantum associative memories with their experimental realizations. Therefore, a thorough understanding of how accessing non-local models such as the one presented in this manuscript, via likewise non-local effective atomic interactions such us the ones emerging in reference [68], is a promising question to be addressed.…”

Section: Discussionmentioning

confidence: 99%

Phase diagram of quantum generalized Potts-Hopfield neural networks

2022

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We introduce and analyze an open quantum generalization of the q-state Potts-Hopfield neural network, which is an associative memory model based on multi-level classical spins. The dynamics of this many-body system is formulated in terms of a Markovian master equation of Lindblad type, which allows to incorporate both probabilistic classical and coherent quantum processes on an equal footing. By employing a mean field description we investigate how classical fluctuations due to temperature and quantum fluctuations effectuated by coherent spin rotations affect the ability of the network to retrieve stored memory patterns. We construct the corresponding phase diagram, which in the low temperature regime displays pattern retrieval in analogy to the classical Potts-Hopfield neural network. When increasing quantum fluctuations, however, a limit cycle phase emerges, which has no classical counterpart. This shows that quantum effects can qualitatively alter the structure of the stationary state manifold with respect to the classical model, and potentially allow one to encode and retrieve novel types of patterns.

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Section: Discussionmentioning

confidence: 99%