Statistical Challenges and Opportunities in Quantum Computing: A Review

Wanjiku Amani Njoki

Faculty of Engineering Kampala International University Uganda

ABSTRACT

Quantum computing represents a transformative paradigm shift in computational capabilities by leveraging quantum mechanical principles such as superposition and entanglement. This article explores the intersection of quantum computing with statistical methods, focusing on key areas such as quantum error correction (QEC), statistical inference, data interpretation, and optimization of quantum algorithms. Quantum error correction is essential due to quantum systems’ susceptibility to errors, requiring advanced statistical techniques for error detection and mitigation without collapsing quantum states. Statistical inference and data interpretation in quantum computing face challenges posed by the probabilistic nature of quantum data, necessitating novel statistical frameworks for accurate analysis and prediction. Optimizing quantum algorithms involves refining existing algorithms like Shor’s and Grover’s, developing new algorithms through statistical principles, and analyzing performance using statistical methods. Integration of classical and quantum approaches enhances algorithmic efficiency and reliability. Furthermore, quantum machine learning (QML) and big data analytics capitalize on quantum computing’s potential to process vast datasets efficiently, underpinned by statistical methodologies for algorithm optimization and data management. Despite challenges such as quantum hardware limitations and noise interference, ongoing research aims to advance statistical frameworks, optimize algorithms, and explore new applications, ensuring statistical methods remain pivotal in harnessing the full potential of quantum computing across diverse domains.

Keywords: Quantum Computing; Statistical Methods, Quantum; Error Correction; Quantum Machine Learning; Big Data Analytics

CITE AS: Wanjiku Amani Njoki (2024). Statistical Challenges and Opportunities in Quantum Computing: A Review. IDOSR JOURNAL OF COMPUTER AND APPLIED SCIENCES 9(1):-33-37, 2024. https://doi.org/10.59298/JCAS/2024/91.153337001