Quantum Machine Learning (QML) is an emerging field of research which lies at the intersection of Quantum Mechanics, Artificial Intelligence and Information Theory. QML takes the leverage of inherent quantum nature of the real-world systems. Quantum mechanics allows us to make use of quantum parallelism, superposition, quantum capacity and quantum security. In this proposed work, applications of QML in electrical engineering have been demonstrated. Two examples in power systems, one being load forecasting using quantum K-Nearest Neighbors (KNN) and another being load flow analysis using quantum power flow algorithm are considered. Our simulations in both cases reveal that a quantum speed-up is possible for our applications over their classical counterpart. In the first application, speed up from O(N*M) in KNN to O(√(N*M)) in quantum KNN is observed, and the final classification is obtained with an accuracy of 62%. In the second application, it is observed Harrow-Hassidim-Llyod (HHL) algorithm outperforms all classical algorithms under consideration for load flow analysis with an execution time of approximately 0.00051 s. These results showcase a great scope in quantum machine learning and quantum computing for the future.