Quantum State Fidelity for Functional Neural Network Construction

TL;DR

This paper explores the use of hybrid quantum algorithms to construct neural networks from high-dimensional neural data, showing they can be competitive with classical methods and offer new insights.

Contribution

It introduces quantum state fidelity methods for neural network construction, demonstrating their effectiveness compared to classical techniques.

Findings

Quantum methods reveal distinct functional networks.

Quantum algorithms are competitive with classical techniques.

Quantum computing offers advantages in high-dimensional data analysis.

Abstract

Neuroscientists face challenges in analyzing high-dimensional neural recording data of dense functional networks. Without ground-truth reference data, finding the best algorithm for recovering neurologically relevant networks remains an open question. We implemented hybrid quantum algorithms to construct functional networks and compared them with the results of documented classical techniques. We demonstrated that our quantum state fidelity methods can provide competitive alternatives to classical metrics by revealing distinct functional networks. Our results suggest that quantum computing offers a viable and potentially advantageous alternative for data-driven modeling in neuroscience, underscoring its broader applicability in high-dimensional graph inference and complex system analysis.