Entangling power of neural networks

• URL: http://arxiv.org/abs/2608.05293v1
• Date: Wed, 05 Aug 2026 18:00:06 GMT
• Title: Entangling power of neural networks
• Abstract summary: We introduce the entangling power of an encoder-decoder neural network, which quantifies its ability to generate entanglement between subsystems.<n>Our work provides a framework for analyzing correlations in machine learning that generalizes the notion of the Schmidt rank in entanglement theory.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Characterizing the complexity of correlations between subsystems is a fundamental task across information theory, machine learning, and science. In quantum physics, neural networks have found increasing application in learning wavefunctions. Here we introduce the entangling power of an encoder-decoder neural network, which quantifies its ability to generate entanglement between subsystems, dependent on a latent space dimension $K$ and the complexity class of the decoder. We exactly calculate this quantity for polynomial decoders of degree $p$ acting on a $K$-dimensional latent space. Our results establish the exponential entangling power of neural networks with modest resources. More broadly, our work provides a framework for analyzing correlations in machine learning that generalizes the notion of the Schmidt rank in entanglement theory.

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