Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks

Abstract

Classical artificial neural networks, built from perceptrons as their elementary units, possess enormous expressive power. Here we investigate a quantum neural network architecture, which follows a similar paradigm. It is structurally equivalent to so-called (1+1)D quantum cellular automata, which are two-dimensional quantum lattice systems on which dynamics takes place in discrete time. Information transfer between consecutive time slices -- or adjacent network layers -- is governed by local quantum gates, which can be regarded as the quantum counterpart of the classical perceptrons. Along the time-direction an effective dissipative evolution emerges on the level of the reduced state, and the nature of this dynamics is dictated by the structure of the elementary gates. We show how to construct the local unitary gates to yield a desired many-body dynamics, which in certain parameter regimes is governed by a Lindblad master equation. We study this for small system sizes through numerical simulations and demonstrate how collective effects within the quantum cellular automaton can be controlled parametrically. Our study constitutes a step towards the utilisation of large-scale emergent phenomena in large quantum neural networks for machine learning purposes.

17 pages, 3 figures

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Cited by in corpus (3)

• Non-linear classification capability of quantum neural networks due to emergent quantum metastability
• Strong symmetries in collision models and physical dilations of covariant quantum maps
• Nonequilibrium phases and quantum correlations in synthetic transport models