MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641078657 A) filed by Dr. Mahesh Kotha; Angari Kiran Kumar; A Prashanthi; Dr. Annapurna Gummadi; and Ravindra Changala on June 25, 2026, for Edge-Enabled Quantum Learning Framework For Smart City Applications.

Inventors include Dr. Mahesh Kotha; Angari Kiran Kumar; A Prashanthi; Dr. Annapurna Gummadi; and Ravindra Changala.

The application for the patent was published on July 03, 2026, under issue no. 27/2026.

Abstract: Abstract of Invention: The Edge-Enabled Quantum Learning Framework (EQLF) is a hybrid system integrating edge nodes with quantum simulators or NISQ devices for smart city applications. It comprises: (1) IoT data ingestion layer with preprocessing at edge gateways; (2) Quantum feature mapping using amplitude embedding or variational quantum circuits for dimensionality reduction; (3) Hybrid QML models (e.g., Quantum Support Vector Machines, Quantum Neural Networks, Variational Quantum Eigensolver for optimization) trained/fine-tuned via classical-quantum loops; (4) Federated learning for privacy-preserving model updates across city nodes; (5) Real-time inference engine for applications like traffic optimization, energy grid management, anomaly detection in security feeds, and predictive maintenance. Novel aspects include quantum-inspired pruning and compression for resource-constrained edge devices, achieving up to 90%+ memory savings while maintaining high accuracy (e.g., 98% in anomaly detection). The framework uses hybrid architectures where classical edge devices handle data encoding/preprocessing and quantum kernels accelerate core computations. It supports post-quantum cryptography for secure communications. Deployment on simulators like Qiskit allows immediate practicality, with pathways to hardware integration. Benefits include reduced latency ( 10ms for critical decisions), lower energy consumption, enhanced scalability for millions of devices, and superior handling of uncertainty/noise in urban data. Case studies demonstrate improvements in traffic flow (20-30% reduction in congestion) and energy efficiency. The invention advances sustainable smart cities by enabling autonomous, intelligent infrastructure.

Disclaimer: Curated by HT Syndication.