Quantum Machine Learning Algorithms: Evaluating Performance and Scalability in Noisy Intermediate-Scale Quantum Devices

Abstract

Data analysis and model training could be completely transformed by Quantum Machine Learning (QML), which integrates the concepts of quantum computing with machine learning. It is becoming more and more important to assess the performance and scalability of QML algorithms as we enter the age of Noisy Intermediate-Scale Quantum (NISQ) devices. looks into different quantum machine learning techniques, such as variational quantum eigensolvers, quantum neural networks, and quantum support vector machines, and how well they work on NISQ architectures. Using metrics for precision, computational economy, and robustness to noise, we evaluate their performance on various datasets. While QML algorithms do better than classical algorithms in some cases, our research shows that the noise in NISQ devices often limits their scalability. We discover methods, such hybrid quantum-classical approaches and error mitigation strategies, to make QML algorithms more robust. the present state of QML in NISQ settings, outlining its strengths and weaknesses, therefore paving the way for quantum-enhanced machine learning to improve and find real-world use in many domains.