Artificial Intelligence & Quantum Computing for Causal Loop and Chronology Violation Detection

A Deep Learning Approach to Predicting Traversable Wormhole Stability and Temporal Paradoxes

Abstract

As admissible solutions to Einstein’s field equations, traversable wormholes present the prospect of non-trivial topological structures tying disparate areas of spacetime together.  Their stability is seriously questioned because their theoretical existence, which is determined by the Morris-Thorne metric, requires the inclusion of exotic matter that violates the energy conditions.  These structures also allow for the creation of closed timelike curves (CTCs), which could violate causality and cause paradoxes, undermining the basic tenets of chronology protection.  Although different gravity models and semiclassical quantum effects suggest ways to maintain wormholes, it is still unclear how to precisely formulate stability criteria and causal consistency.  In order to predict wormhole stability and identify the emergence of causal loops, this study makes use of deep learning techniques and artificial intelligence (AI). In order to evaluate the effect of exotic matter distributions on stability, Einstein’s field equations are numerically solved using Physics-Informed Neural Networks (PINNs) under dynamic boundary conditions.  Potential CTC formations and self-consistency violations are detected by tracing geodesic structures using Graph Neural Networks (GNNs), Quantum Neural Network (QNNs)and Recurrent Neural Networks (RNNs).  Furthermore, the exotic matter configuration is optimised via reinforcement learning (RL) techniques to minimise instabilities while maintaining traversability.  This research advances the intersection of machine learning, general relativity, and quantum field theory in the study of spacetime topology and causality, analyses chronology protection mechanisms, and evaluates wormhole viability by fusing relativistic physics with AI-driven computational techniques.