Abstract
Quantum Machine Learning (QML) represents a new paradigm that combines quantum computing with classical machine learning to address complex computational problems. Yet, the effective utilization of QML systems is limited because of the noise naturally arising from quantum hardware and the uncertainty in data and model predictions. Uncertainty-aware QML: improving reliability and robustness in noise-prone environments (Reprint from Plops ONE)† — Introduction In this work we investigate uncertainty-aware quantum-machine learning (QML), with a specific emphasis on how to improv.Quantum systems are prone to DE coherence, gate errors and interaction with the environment, inducing noise that causes uncertainty in computational results. Finally, given that classical data used to train QML models is typically noisy by nature, the errors are compounded. This highlights the demand of uncertainty-aware frameworks that can capture and mitigate both quanta as well us classical uncertainties. In this paper we gather an extensive review of uncertainty embedding techniques: Probabilistic methods, Bayesian inference and hybrid quantum-classical methods.
The overview also covers noise-aware architectures like quantum neural networks (QNNs), and quantum support vector machines (QSVM) or Variational quantum circuits. Additionally, the study examines sophisticated approaches including error mitigation, uncertainty quantification as well as robust optimization methods to improve decision-making reliability. The practical significance of uncertainty-aware QML is illustrated by examining applications in healthcare diagnostics, cybersecurity, finance, and autonomous systems. The paper wraps up with important research bottlenecks and future directions, including scalable quantum hardware, better noise modelling and explainable AI integration. This work contributes to the development of trustworthy QML systems that can make reliable decisions in practice by addressing uncertainty and noise jointly.
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