Training the classification capability of large-scale quantum cellular automata

• URL: http://arxiv.org/abs/2509.18262v1
• Date: Mon, 22 Sep 2025 18:00:06 GMT
• Title: Training the classification capability of large-scale quantum cellular automata
• Authors: Mario Boneberg, Simon Kochsiek, Gabriele Perfetto, Igor Lesanovsky,
• Abstract summary: In the vicinity of a phase transition ergodicity can be broken. Here, different initial many-body configurations evolve towards one of several fixed points, which are macroscopically distinguishable through an order parameter.<n>We demonstrate that this capability can be efficiently learned from training data even in extremely high-dimensional state spaces.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the vicinity of a phase transition ergodicity can be broken. Here, different initial many-body configurations evolve towards one of several fixed points, which are macroscopically distinguishable through an order parameter. This mechanism enables state classification in quantum cellular automata and feed-forward quantum neural networks. We demonstrate that this capability can be efficiently learned from training data even in extremely high-dimensional state spaces. We illustrate this using a quantum cellular automaton that allows binary classification, which is closely connected to the dynamics of a $\mathbb{Z}_2$-symmetric Ising model with local interactions and dissipation. This approach can be generalized beyond binary classification and offers a natural framework for exploring the link between emergent many-body phenomena and the interpretation of data processing capabilities in the context of quantum machine learning.

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