# Searching for Quantum Effects in the Brain: A Bell-Type Test for Nonclassical Latent Representations in Autoencoders

arXiv: https://arxiv.org/abs/2601.10588 · HTML: https://mlsanity.com/paper/2601.10588/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 86 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [A high-bias, low-variance introduction to Machine Learning for physicists](https://mlsanity.com/paper/1803.08823/) (2018)
- [Machine learning & artificial intelligence in the quantum domain](https://mlsanity.com/paper/1709.02779/) (2017)
- [On compression rate of quantum autoencoders: Control design, numerical and experimental realization](https://mlsanity.com/paper/2005.11149/) (2020)
- [Applying Quantum Autoencoders for Time Series Anomaly Detection](https://mlsanity.com/paper/2410.04154/) (2024)
- [Quantum Cognition: The possibility of processing with nuclear spins in the brain](https://mlsanity.com/paper/1508.05929/) (2015)
- [Quantum effects in the brain: A review](https://mlsanity.com/paper/1910.08423/) (2019)
- [Quantum Models of Consciousness from a Quantum Information Science Perspective](https://mlsanity.com/paper/2501.03241/) (2024)
- [Quantum information theoretic approach to the hard problem of consciousness](https://mlsanity.com/paper/2504.18550/) (2025)

## Abstract (arXiv)

Whether neural information processing is entirely classical or involves quantum-mechanical elements remains an open question. Here we propose a model-agnostic, information-theoretic test of nonclassicality that bypasses microscopic assumptions and instead probes the structure of neural representations themselves. Using autoencoders as a transparent model system, we introduce a Bell-type consistency test in latent space, and ask whether decoding statistics obtained under multiple readout contexts can be jointly explained by a single positive latent-variable distribution. By shifting the search for quantum-like signatures in neural systems from microscopic dynamics to experimentally testable constraints on information processing, this work opens a new route for probing the fundamental physics of neural computation. The proposed test identifies violations of classical latent-variable consistency at the level of statistical representations, without assuming a specific underlying physical mechanism.