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Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics

Statistical mechanics of a Hopfield neural-network model in a transverse field

Yu‐qiang Ma, Yueming Zhang, Y. G., Chang-De Gong

Published Jun 1, 1993• 10 citations
Publisher

Abstract

We construct a soluble model to describe the retrieval properties of the quantum transverse Hopfield model in neural networks. A set of equations for the order parameters describing retrieval and spin-glass phases of the Hopfield model in a transverse field are obtained. Based on these equations, phase diagrams are examined and the memory storage capacity of the networks is analyzed as a function of the temperature and transverse field.

DOI
10.1103/physreve.47.3985

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We construct a soluble model to describe the retrieval properties of the quantum transverse Hopfield model in neural networks. A set of equations for the order parameters describing retrieval and spin-glass phases of the Hopfield model in a transverse field are obtained. Based on these equations, phase diagrams are examined and the memory storage capacity of the networks is analyzed as a function of the temperature and transverse field.
The Hopfield model in a transverse field is investigated in order to clarify how quantum fluctuations affect the macroscopic behavior of neural networks. Using the Trotter decomposition and the replica method, we find that the ααα (the ratio of the number of stored patterns to the system size)-ΔΔΔ (the strength of the transverse field) phase diagram of this model in the ground state resembles the ααα-TTT phase diagram of the Hopfield model quantitatively, within the replica-symmetric and static approximations. This fact suggests that quantum fluctuations play quite similar roles to thermal fluctuations in neural networks as long as macroscopic properties are concerned.
We examine a quantum Hopfield neural-network model in the presence of trimodal random transverse fields and random neuronal thresholds within the method of statistical physics. We use the Trotter decomposition to map the problem into an equivalent classical random Hopfield-type Ising model and obtain phase transitions between the ferromagnetic retrieval and the paramagnetic phases. The influence of competition between the diluted random transverse fields and the diluted random thresholds on the system is discussed, and some interesting results such as tricritical points and reentrance are analyzed.
A quantum Hopfield spin-glass model with transverse fields is presented. A set of equations for the order parameters of a large class of spin-glass models with transverse fields is obtained, and the phase transitions are analyzed with a finite number of patterns p. We find that a reentrant phenomenon is possible for a quantum Mattis model with transverse fields, contrary to the quantum transverse Sherrington-Kirkpatrick-like model. The effects of quantum fluctuations (induced by the transverse field) on the ordered phases of systems with competing interactions are also discussed.
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.