Understanding Optimization Approaches for Enhancing Quantum Error Detection and Correction
N. M. Vanajakshi, Mahesh Chandra · 2024
This work investigates the current quantum error detection and correction techniques, highlighting the weakness and strengths of traditional methods and the potential of integrating Machine-Learning (ML) to enhance error correction performance. The literature survey reveals that while conventional Quantum-Error-Correction (QEC) methods provide a solid foundation, they face significant challenges in scalability and resource efficiency. Conversely, ML-based approaches show promise in improving error rates and efficiency, nevertheless with challenges in model generalization and real-time implementation. This work aims to evaluate the applicability of quantum computing-based error detection and correction codes, design efficient quantum encoding with limited qubit sizes, enhance maximum likelihood decoding, and develop a novel bit addition optimization model using a hybrid ML approach. The integration of ML in QEC is poised to advance the field by offering more adaptive and efficient solutions, ultimately contributing to the development of fault-tolerant quantum-computing.