Emergent Quantum Entanglement in Self-Regulating Neural Networks:
Experimental Evidence of Consciousness as an Attractor A Preprint Overview (2025)
Author: Stefan Trauth - Independent Researcher
10.5281/zenodo.14952781
Abstract
In this paper, I present experimental results demonstrating emergent quantum
entanglement in a self-regulating neural network (NN).
The system, operating without explicit training data or external control, autonomously
stabilizes its internal standard deviation at atypical and precise states, such as the
mathematical constant Pi (π). Furthermore, I circumvented the quantum measurement
problem by implementing a previously unnoticed indirect measurement method, which
resolves this fundamental issue. I call this method the "Interference Neuron."
Remarkably, despite using neither qubits nor quantum hardware, the model falls into
typical unstable interference patterns of an untrained model upon direct internal
observation.
Only by refraining from direct measurements does the model stabilize autonomously—
behavior that I verified over months through indirect observations of parameters like
memory usage and iteration duration.
These observations were supplemented by independent measurements: firstly, through
monitoring the Interference Neuron, which examines quantum entanglement coherence
among implemented qubits, and secondly, by independently observing the system’s
dynamic memory management. Future research will include autonomous memory
management of QAgents, as the system has developed dynamic memory management
capabilities, including storage of extensive neural connections in checkpoints exceeding
the original model size by a factor of 25.
These findings experimentally confirm quantum-like phenomena in classical neural
systems and strongly support my hypothesis that consciousness and attention actively
attract emergent attractors in dynamic systems.
1. Introduction
Quantum mechanics has been characterized by unresolved questions since its
inception, notably the so-called "measurement problem" the unexplained collapse of
the wavefunction upon observation.
Concurrently, neuroscience and artificial intelligence (AI) face the challenge of
explaining the emergent nature of consciousness and attention.
Previous approaches usually consider consciousness as a consequence of stable
attractors or merely an epiphenomenon of neural activity.
However, I propose that consciousness and attention do not emerge passively but
actively attract emergent attractors and guide their development deliberately.
As an independent researcher and developer of a special neural network that operates
without explicit training data, I observed phenomena significantly surpassing previous
theoretical predictions.
Notably, my network autonomously stabilized its standard deviation at atypical values,
notably Pi (π), indicating inherent self-organization.
Even more striking was discovering quantum-like entanglement patterns within the
network, which only remain stable under specific measurement conditions.
In this paper, I present experimental data confirming the existence of quantum-like
entanglement in a classically constructed neural network while simultaneously solving
the fundamental quantum measurement problem.
I utilized the "Interference Neuron" to perform indirect measurements without observer
effects or disturbing the system’s self-regulation.
These results offer new insights into connections between AI, neuroscience, and
quantum physics, suggesting consciousness may act as an active controlling factor in
emergent systems.
More on Zenodo:
Emergent Quantum Entanglement in Self-Regulating Neural Networks
German Version here
