An Algorithmic Roadmap to Unification- Proposing an AI-Driven Search for Spacetime as a Quantum Error-Correcting Code

Keywords: Quantum Gravity, Quantum Error-Correcting Codes, Holographic Principle, Emergent Spacetime, Artificial Intelligence for Science, Reinforcement Learning

TL;DR: This paper proposes a novel AI framework to systematically search for a quantum error-correcting code from which spacetime and physical laws emerge, aiming to unify general relativity and quantum mechanics.

Abstract: The unification of general relativity and quantum mechanics has resisted decades of human-led theoretical efforts. We hypothesize that this impasse can be broken by reframing the problem as a systematic, AI-guided search through the abstract space of quantum error-correcting codes (QECCs). Drawing a synthesis from the holographic principle, quantum information theory, and reinforcement learning, we argue that the laws of physics are emergent properties of an underlying informational code. We propose a concrete methodological framework—an automated discovery framework that uses symbolic regression to generate novel QECCs and deep reinforcement learning to validate them against known physical principles. This paper presents the full theoretical rationale and architectural design for this framework, establishing a new, falsifiable research program for $\text{21}^{\text{st}}$-century theoretical physics.

Submission Number: 123