International Journal of Science, Engineering and TechnologyAuthors: S.Saranya Devi, K.Shandhini, R.Kalai Selvi, R.Kalai Magal, M.Satheesh
Abstract: Machine-learning algorithms infer a target relationship between inputs and outputs by studying example data, enabling them to interpret previously unseen inputs. This capability is essential for tasks like image and speech recognition, as well as strategy optimization, and it is increasingly important across the IT industry. In recent years, researchers have explored whether quantum computing can enhance classical machine-learning methods. Proposed ideas include accelerating computationally expensive algorithms or their subroutines using quantum hardware, and reformulating stochastic techniques within a quantum-theoretic framework.
