Open quantum systems composed of atoms interacting with light exhibit behaviour that is akin to that of associative memories [1]. This means that they possess stationary states that can be interpreted as stored memory patterns, which are retrieved when the initial state is inside the basin of attraction of a given pattern [2]. The corresponding pattern retrieval dynamics can be observed in actual experimental settings. In these experiments atoms are confined within an optical cavity whose photons mediate long-range interactions [3]. Stored patterns are encoded in the atom-light coupling constants. This setting offers an interesting opportunity for studying quantum generalisations of associative memories and stored (quantum) patterns in this context [4]. Moreover, it allows to systematically construct scenarios in which quantum effects might be beneficial, e.g., for speeding up the pattern retrieval process [5]. I will talk about recent research of my group on this subject, which builds a bridge between classic machine learning concepts, such as the Hopfield Neural Network, and most recent experimental manifestations of synthetic quantum matter.

1. E. Fiorelli et al., Physical Review Letters 125, 070604 (2020)
2. F. Carollo and I. Lesanovsky, arXiv:2009.13932 (2020)
3. V. D. Vaidya et al., Physical Review X 8, 011002 (2018)
4. P. Rotondo et al., Journal of Physics A 51, 115301 (2018)
5. E. Fiorelli et al., Physical Review A 99, 032126 (2019)