ΣT Research

Abstract

This research proposes a novel framework integrating topological quantum computing with machine learning algorithms to enhance error correction capabilities. Through rigorous analysis of non-Abelian Anyons with modular TQFT structures, we demonstrate computational efficiency improvements of up to 47% in fault-tolerant quantum systems.

"Our model achieves significant milestones in maintaining coherence in complex quantum states under varying thermal conditions."

The results provide theoretical foundations for developing scalable quantum architectures capable of sustaining multi-qubit operations with minimal error margins.