MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541122958 A) filed by Vellore Institute Of Technology, Vellore, Tamil Nadu, on Dec. 5, 2025, for 'quantum machine learning system for encrypted network traffic analysis.'
Inventor(s) include Dr. Annapurna Jonnalagadda; Dr. Aswani Kumar Cherukuri; Akshay Murthy; and Gokul Sunil Sodar.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The present disclosure provides a computer-implemented method (100) for analyzing encrypted network traffic. The method (100) loads (102) an encrypted network traffic dataset, applies (112) quantum feature encoding to transform classical features into quantum state representations, and trains (114) quantum machine learning models using the quantum-encoded features to classify the encrypted network traffic. The quantum feature encoding includes angle encoding that maps classical data values to rotation angles of quantum gates or amplitude encoding that embeds classical data into quantum state amplitudes. The quantum machine learning models include hybrid quantum-classical models, a quantum support vector machine (300) utilizing quantum kernel functions, and a quantum k-nearest neighbors classifier (400) using quantum fidelity measurements as distance metrics. The quantum circuits incorporate entangling layers using CNOT gates and rotation gates to create quantum correlations between encoded features."
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