TY  - JOUR
AU  - Bödeker, Lukas
AU  - Fiorelli, Eliana
AU  - Müller, Markus
TI  - Optimal storage capacity of quantum Hopfield neural networks
JO  - Physical review research
VL  - 5
IS  - 2
SN  - 2643-1564
CY  - College Park, MD
PB  - APS
M1  - FZJ-2023-01868
SP  - 023074
PY  - 2023
AB  - Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here quantum generalizations of classical networks realizing associative memories—capable of retrieving patterns, or memories, from corrupted initial states—have been proposed. It is a challenging open problem to analyze quantum associative memories with an extensive number of patterns and to determine the maximal number of patterns the quantum networks can reliably store, i.e., their storage capacity. In this work, we propose and explore a general method for evaluating the maximal storage capacity of quantum neural network models. By generalizing what is known as Gardner's approach in the classical realm, we exploit the theory of classical spin glasses for deriving the optimal storage capacity of quantum networks with quenched pattern variables. As an example, we apply our method to an open-system quantum associative memory formed of interacting spin-1/2 particles realizing coupled artificial neurons. The system undergoes a Markovian time evolution resulting from a dissipative retrieval dynamics that competes with a coherent quantum dynamics. We map out the nonequilibrium phase diagram and study the effect of temperature and Hamiltonian dynamics on the storage capacity. Our method opens an avenue for a systematic characterization of the storage capacity of quantum associative memories.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000985637100010
DO  - DOI:10.1103/PhysRevResearch.5.023074
UR  - https://juser.fz-juelich.de/record/1006815
ER  -