The Mechanics of Quantum Entanglement in Neural Networks

1. Abstract

The intersection of quantum computing mechanics and deep neural networks represents a frontier in machine learning. In this paper, we explore how mapping network weights to state vectors can reduce computational overhead during backpropagation by a factor of $O(\log n)$.

2. Introduction to State Vectors

Traditional neural networks rely on classical bits. By shifting our paradigm to qubits, we allow for states of superposition during the training phase. As noted in previous literature [1], this requires a fundamental restructuring of the loss function.

3. Methodology & Framework

We utilized a 16-qubit simulated environment running in parallel with a standard PyTorch tensor infrastructure. The alignment of these two systems was managed by a custom middleware layer...