Paper #1

Towards a Quantum Field Theory for Optical Artificial Intelligence

Antonio Manzalini

Abstract: Today, several socio-techno-economic drivers are steering the evolution of Telecommunications and Internet towards a growing exploitation of ultra-broadband infrastructures (e.g., 5G) and Artificial Intelligence (AI) systems. Focusing on the most promising AI technological approaches, Deep Neural Networks (DNNs) are outperforming in several applications domains. One of the possible explanations, elaborated in literature, is that DNN functioning is deeply rooted in the principles of theoretical Physics, specifically Quantum Field Theory (QFT) and Gauge theory. This is encouraging even more researches and experiments in the direction of a full exploitation of quantum computing and networking for the development of innovative Information Communication Technologies (ICT) and AI systems. In this innovation avenue, given that QFT and Gauge theory have been already proposed for modeling the brain and biological nervous systems, this paper explores the intriguing possibility of exploiting QFT principles also for future DNN, for instance by using electromagnetic waves effects in metamaterials. This appears to be a promising direction of future studies and experiments: therefore, the paper also describes the architecture of a simple optical DNN prototype, based on metamaterials, which is intended as a live test-bed, for simulations and experiments.

Keywords: Artificial Intelligence; Deep Learning; Deep Neural Networks; Gauge Theory; Quantum Field Theory.