MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641051535 A) filed by Aditya College Of Engineering, Chittoor, Andhra Pradesh, on April 22, for 'a system and method for efficient quantum state encoding and feature mapping for scalable quantum machine learning applications.'
Inventor(s) include Dr. Nagasubba Rayudu P; Dr. P. Gangadhara Reddy; Dr. J. Pradeep; and Dr. D. Sreenivasulu Reddy.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "A system and method for efficient quantum state encoding and feature mapping for scalable quantum machine learning applications are disclosed. The invention provides an integrated framework for transforming classical input data into optimized quantum representations using adaptive encoding techniques, reduced circuit-depth strategies, and hardware-aware qubit allocation mechanisms. A quantum feature mapping engine employs parameterized circuits, entanglement-assisted transformations, and quantum kernel methods to project data into enhanced Hilbert spaces for improved classification, regression, and pattern recognition performance. The system further incorporates hybrid quantum-classical optimization modules configured to iteratively refine encoding parameters, feature mappings, and model outputs while minimizing computational overhead. Error mitigation mechanisms, including noise-aware calibration, probabilistic correction, and measurement optimization, improve execution reliability across noisy quantum environments. Real-time processing is supported through modular orchestration layers enabling low-latency inference, scalable workload distribution, and interoperability across multiple quantum processors and software frameworks. The invention further supports adaptive learning, distributed quantum execution, and compatibility with variational quantum algorithms for diverse analytical applications. By reducing gate complexity, improving encoding fidelity, and enhancing scalability, the proposed framework addresses limitations of conventional quantum learning architectures. The invention provides a robust, efficient, and scalable solution for practical deployment of quantum machine learning in scientific computing, optimization, cybersecurity, financial modeling, and advanced intelligent data analytics applications."
Disclaimer: Curated by HT Syndication.
