Quantum Physics Interface to Data Science, Artificial Intelligence, and Spacekime Analytics

Physics is in the core of many data-intensive research activities; from governing molecular interactions, to modeling social behavior networks, enabling solid-state data storage, facilitating Ising modeling of numerical simulations, and underpinning Metropolis–Hastings estimation and optimization in machine learning (ML) and artificial intelligence (AI) applications. This talk will present a direct connection between quantum mechanical principles, data science foundations, and statistical inference on longitudinal processes.

By extending the physical concepts of time, events, particles, and wavefunctions to their AI counterparts – complex-time (kime), complex-events (kevents), data, and inference-functions – spacekime analytics provides a new foundation for representation, modeling, analyzing, and interpreting dynamic high-dimensional data. We will show the effects of kime-magnitude (longitudinal time order) and kime-direction (phase) on AI predictive analytics, forecasting, regression, classification, and scientific inference.

The mathematical foundation of spacekime analytics also provide mechanisms to introduce spacekime calculus, expand Heisenberg’s uncertainty principle to reveal statistical implications of inferential uncertainty, and a develop a Bayesian formulation of spacekime inference. Lifting the dimension of time opens a number of challenging theoretical, experimental, and computational data science problems. It leads to a new representation of commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold. Using simulated data and clinical observations (e.g., structural and functional MRI), we will demonstrate alternative strategies to transform time-varying processes (time-series) to kime-surfaces and show examples of spacekime analytics.