Self-Organizing Quantum Neural Networks for Feature Discovery

Abstract

This paper investigates the feasibility of leveraging quantum entanglement to facilitate self-organizing feature discovery within neural networks. Traditional neural networks rely on gradient-based optimization to learn feature representations, often requiring substantial computational resources and careful hyperparameter tuning. We propose a novel quantum neural network (QNN) architecture designed to exploit the inherent correlations provided by quantum entanglement. The core idea is that dynamic entanglement between qubits will intrinsically adapt the network's weights, leading to the emergence of richer, more relevant feature representations directly from the data. Specifically, we outline a network structure incorporating controlled quantum gates and measurement operations to induce entanglement. The theoretical framework demonstrates how this entanglement can evolve to capture underlying data correlations, effectively performing unsupervised feature learning. We detail the mathematical formulation of the network's dynamics, emphasizing the role of entanglement entropy as a key indicator of feature discovery. While experimental validation is not included in this theoretical exploration, this work lays the groundwork for future investigations into the potential of quantum neural networks for efficient and adaptive feature extraction. The key concepts involve the construction of a QNN with entangled qubits, the evolution of entanglement under data input, and the quantification of feature discovery through entanglement entropy.