MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641076909 A) filed by Sr University on June 22, 2026, for An Integrating Large Language Models With Quantum Machine Learning For Stock Market Simulation, Prediction, And Optimization.
Inventors include K Srinivasa Rao; and Pramod Patro.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: INTEGRATING LARGE LANGUAGE MODELS WITH QUANTUM MACHINE LEARNING FOR STOCK MARKET SIMULATION, PREDICTION, AND OPTIMIZATION In order to simulate, forecast, and optimize stock market behaviors for better financial decision-making, the current invention reveals a single intelligent framework that combines Quantum Machine Learning (QML) approaches with Large Language Models (LLMs). This hybrid system processes and extracts contextual information from a variety of financial texts, like news, earnings reports, and social media, by utilizing the natural language processing powers of LLMs. Simultaneously, QML modules assess real-time financial indicators, price trends, and market volatilities using state-space modeling and quantum-enhanced pattern recognition. An LLM-based contextual analyzer, a QML-driven predictive core, a data ingestion pipeline, and a decision optimization layer make up the integrated architecture. This innovation offers a scalable and flexible method for producing investment strategies, portfolio risk evaluations, and high-confidence trading signals. The invention greatly improves prediction accuracy, lowers computational latency, and makes explainable decision logic possible by combining the benefits of quantum computing with symbolic and sub-symbolic intelligence paradigms. Central banks, retail investment platforms, and hedge funds all use this method. The new aspect is the smooth integration of quantum inference with language comprehension in an ongoing learning cycle. Additionally, the innovation facilitates simulation-driven policy improvement through reinforcement learning optimized with quantum feedback. The framework is compatible with both cloud-based and edge quantum processors and is made to be hardware-independent.
Disclaimer: Curated by HT Syndication.
