Shubham Sharma

Context: The rapid increase in cyber threats, coupled with increasingly sophisticated attack strategies and the exponential growth of data in recent years, has exposed significant limitations in classical machine learning, rule‐based, and signature‐based defense mechanisms. These approaches are often unable to scale or adapt effectively to evolving threat landscapes. Objective: As a result, Quantum Machine Learning (QML) has emerged as a promising alternative, leveraging computational principles from quantum mechanics. QML enables more expressive encoding and efficient processing of high‐dimensional data, offering potential advantages for complex cybersecurity tasks. Method: This survey provides a comprehensive overview of QML techniques relevant to the domain of security, such as Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and Quantum Generative Adversarial Networks (QGANs), and discusses the contributions of this paper in relation to existing research in the field and how it improves over them. Results: It also maps these methods across supervised, unsupervised, and generative learning paradigms, as well as to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted‐traffic analytics. It also discusses their application in cloud computing security, where QML can enhance secure, scalable operations. Conclusion: Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.

Machine Learning has evolved significantly over the last decade, with models capable of robust and accurate predictions over data of various categories. The impact is rather big in the field of Healthcare. However, deployment of small, energy-efficient models remains a goal pursued by researchers across the field. Futhermore, the model inferences are hard to interpret and draw conclusions from, especially in healthcare, where its very important to know how the diagnosis decision was passed and what factors are causing the issues. In this paper, we address both of these shortcomings, where we develop a neuromorphic computing based machine learning model, namely an SNN, and using SHAP, LIME, and eli5 explainability techniques to explain the predictions of the Spiking Neural Network. The proposed SNN performs at an accuracy of 85.06%, better than the deep neural network by 5.73% for the Diabetes dataset and at an accuracy of 97.83% for the Mobile Health dataset, thereby mitigating the issue of both lack of performance-cum-efficiency, as well as uninterpretability of these machine learning models.