Enhancing associative memory recall and storage capacity using confocal cavity QED
Brendan P. Marsh, Yudan Guo, Ronen M. Kroeze, Sarang Gopalakrishnan,, Surya Ganguli, Jonathan Keeling, and Benjamin L. Lev

TL;DR
This paper proposes a confocal cavity QED neural network platform for associative memory that leverages deterministic dynamics to enhance memory storage and retrieval, even in spin glass regimes, using recent experimental technologies.
Contribution
It introduces a novel confocal cavity QED scheme with deterministic steepest-descent dynamics, improving memory recall beyond traditional models and enabling retrieval in spin glass phases.
Findings
Deterministic steepest-descent dynamics outperform Glauber dynamics in memory recall.
The scheme can store and retrieve memories in spin glass regimes.
Experimental feasibility is supported by recent cavity and Bose-condensed atom technologies.
Abstract
We introduce a near-term experimental platform for realizing an associative memory. It can simultaneously store many memories by using spinful bosons coupled to a degenerate multimode optical cavity. The associative memory is realized by a confocal cavity QED neural network, with the cavity modes serving as the synapses, connecting a network of superradiant atomic spin ensembles, which serve as the neurons. Memories are encoded in the connectivity matrix between the spins, and can be accessed through the input and output of patterns of light. Each aspect of the scheme is based on recently demonstrated technology using a confocal cavity and Bose-condensed atoms. Our scheme has two conceptually novel elements. First, it introduces a new form of random spin system that interpolates between a ferromagnetic and a spin-glass regime as a physical parameter is tuned---the positions of ensembles…
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Taxonomy
TopicsPhotoreceptor and optogenetics research · Advanced Fluorescence Microscopy Techniques · Photonic and Optical Devices
