Quantum Error Correction for AI Applications

EasyChair Preprint 14867

Abstract

Quantum error correction (QEC) is a critical component in the development of robust quantum computing systems, crucial for advancing the capabilities of artificial intelligence (AI). As quantum computing evolves, it holds the potential to revolutionize AI by solving complex problems that are currently intractable for classical computers. However, quantum systems are highly susceptible to errors due to decoherence and operational imperfections, which can undermine the reliability and efficiency of quantum algorithms used in AI. This paper explores the intersection of quantum error correction and AI applications, highlighting the fundamental principles of QEC and their implementation in enhancing quantum machine learning and optimization algorithms. We discuss various QEC strategies, including stabilizer codes, topological codes, and surface codes, evaluating their effectiveness in mitigating error rates and improving computational fidelity. Additionally, we address the challenges associated with integrating QEC techniques into quantum AI frameworks and propose potential solutions to overcome these obstacles. By examining recent advancements and future directions, this paper aims to provide a comprehensive overview of how quantum error correction can pave the way for more reliable and powerful AI systems in the quantum era.

Keyphrases: AI applications, Artificial Intelligence (AI), quantum error correction

@booklet{EasyChair:14867,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Quantum Error Correction for AI Applications},
  howpublished = {EasyChair Preprint 14867},
  year      = {EasyChair, 2024}}