Real-World Applications of Quantum-Enhanced Machine Learning Solutions
Koduri Sreelakshmi, Vishal V. Rathi, K. Shanthi +3 more
QML is the quantum machine learning, a new approach to explore the potential of quantum computation allowing us to discover solutions that otherwise would be hard for a classical computer to find. A variety of applied QML areas will be highlighted in the following chapter such as optimization problems NLP, drug discovery. Some foundations of quantum- superposition and entanglement- are designed to provide more efficient approach- towards higher fidelity in all data driven pipelines. This chapter gives some practical use cases for integration of quantum algorithms into pipelines of machine learning as well as some main challenges (i.e., regarding noise, scalability and algorithm selection) concerning it. Hybrid quantum-classical schemes are thought as the key for progress towards practicality on currently available noisy intermediate-scale quantum hardware. In this chapter, we will address QML viewpoints of the operational strategy and future, including potential QML disruptive effects and the future adoption insights of QML.
Advances in computational intelligence and robotics book series•Apr 17, 2025•0 citations