Let's explore each of them separately:
Quantum Electrodynamics (QED): QED is the quantum field theory that describes the interactions between particles such as electrons and photons, incorporating both quantum mechanics and special relativity. It explains the behavior of particles at the quantum scale through principles like superposition, wave-particle duality, and the uncertainty principle. These principles have implications for how particles are described and how they interact.
some researchers have explored the potential of using quantum systems to enhance certain aspects of AI, such as optimization algorithms or machine learning models. Quantum computing, for instance, has the potential to speed up certain types of computations, which could have implications for AI applications.
Relativity: Relativity, encompassing both special relativity and general relativity, provides a framework to understand the behavior of objects in the presence of gravity and at high speeds. Special relativity, developed by Albert Einstein, deals with the behavior of objects moving at constant velocities relative to each other, while general relativity extends this to include the effects of gravity.
Neural networks primarily rely on concepts from linear algebra, calculus, and optimization algorithms to process information and learn patterns from data.
ongoing research explores potential connections between quantum systems and AI to enhance certain aspects of computation and machine learning.