Automated Validation and Optimization of Quantum Error Correction Codes Using Reinforcement Learning and HyperScore-Guided Assessment

**Abstract:** This paper introduces a novel approach for accelerating the design and validation of quantum error correction (QEC) codes. Current QEC code development is largely manual, intensive, and critically limits scalability. Our framework, leveraging reinforcement learning (RL) and a HyperScore-guided assessment pipeline, automates the search and refinement of QEC codes, predicting their performance and robustness far exceeding human capabilities. This approach facilitates the discovery of novel error correction strategies, enabling scalable and reliable quantum computations.