MUMBAI, India, Feb. 13 -- Intellectual Property India has published a patent application (202641009025 A) filed by Dr. R. Amutha; Dr. T. Prabakaran; Mr. Narendiran R; Dr. Deepak Kumar Panda; Ms. Nibedita Sahoo; Dr. Ranjith J; Mr. R Radhakrishnan; Mr. Manikandan L; Mr. R. Srinivasan; and Dr. S. Raja Shree, Bengaluru, Karnataka, on Jan. 29, for 'method for quantum assisted machine learning using variational quantum algorithms.'
Inventor(s) include Dr. R. Amutha; Dr. T. Prabakaran; Mr. Narendiran R; Dr. Deepak Kumar Panda; Ms. Nibedita Sahoo; Dr. Ranjith J; Mr. R Radhakrishnan; Mr. Manikandan L; Mr. R. Srinivasan; and Dr. S. Raja Shree.
The application for the patent was published on Feb. 13, under issue no. 07/2026.
According to the abstract released by the Intellectual Property India: "The present invention relates to a method for quantum-assisted machine learning using variational quantum algorithms (VQAs), wherein classical machine learning workflows are integrated with parameterized quantum circuits to enhance learning efficiency, optimization capability, and predictive performance. The proposed method involves hybrid quantum-classical processing in which input data is encoded into quantum states, processed through a variational quantum circuit with tunable parameters, and optimized iteratively using classical optimization techniques. The invention enables efficient handling of high-dimensional data, complex optimization landscapes, and non-linear correlations while reducing computational complexity when compared to purely classical approaches. The method is applicable to classification, regression, clustering, pattern recognition, and optimization problems across domains such as finance, healthcare, communication systems, material science, and artificial intelligence, and is implementable on near-term noisy intermediate-scale quantum (NISQ) devices as well as future fault-tolerant quantum computers."
Disclaimer: Curated by HT Syndication.
