Simplifying Quantum Multi-class Classification for the NISQ Era Using Error-Code Output Correction

Within the rapidly evolving realms of artificial intelligence and machine learning, quantum computing has emerged as a pivotal frontier. However, its progress is significantly hindered by the prevailing limitations of quantum hardware. This paper introduces an innovative quantum-classical hybrid algorithm, intricately designed for multi-class classification. The algorithm prioritizes memory efficiency, capitalizing on advanced amplitude encoding techniques and a specialized error-code output correction mechanism to reduce the quantum computational resources needed for data processing while maintaining exceptional speed and classification accuracy. Rigorous testing across a range of datasets, varying in complexities like feature count, class number, and data points, yields notable achievements, particularly in simpler datasets. Nevertheless, the presence of errors and occasional failures with more complex datasets highlights the ongoing need for refinement and optimization, positioning the algorithm toward practical applicability in the field.