Quantum Neural Networks in Quantum Field Theories
Exploring the intersection of machine learning and theoretical physics through the lens of non-abelian gauge groups and tensor network decompositions.
Abstract
This post presents a novel framework for modeling lattice QFTs using hybrid quantum-classical architectures. We demonstrate how neural network renormalization can approximate path integrals in 2+1 dimensions while maintaining gauge invariance through parameterized unitary transformations.
Methodology
- Tensor network ansätze for SU(3) representations
- Monte Carlo integration with VQE optimization
- Adiabatic quantum computing simulations
Citations Required
Please reference this work as:
Γασσσά et al. 2025. "Quantum Neural Networks in Non-Abelian Gauge Theories". εγασσσα Research Series.