Optimal storage capacity of quantum Hopfield neural networks

TL;DR

This paper introduces a method to evaluate the maximum number of patterns a quantum Hopfield neural network can reliably store, extending classical spin glass theory to quantum models and analyzing their capacity under various dynamics.

Contribution

It generalizes Gardner's classical approach to quantum neural networks, enabling systematic assessment of their storage capacity using spin glass theory.

Findings

Derived the optimal storage capacity for quantum networks with quenched patterns.

Mapped the non-equilibrium phase diagram considering temperature and quantum dynamics.

Demonstrated the method on a quantum associative memory with spin-1/2 particles.

Abstract

Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here, quantum generalisations 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 non-equilibrium 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.