Viability of perturbative expansion for quantum field theories on neurons

Accelerated progress in machine learning (ML) over the past decade has had significant impact across many research domains, including physics, and has motivated substantial interdisciplinary work. At the intersection of physics and machine learning, two prominent practical questions have emerged: 1. Can techniques from statistical mechanics and the path integral formulation of quantum field theory (QFT) help us build a theoretical understanding of how neural networks learn? 2. Can neural networks be used to facilitate computations in quantum field theory? These two questions are deeply interrelated, and will motivate the questions we explore in this work. The second question itself splits naturally into two subcategories: (a) applied machine learning for physics problems, and (b) the theoretical interplay between machine learning and QFT techniques. The area of applied ML to physics has already seen considerable progress.