Data-Assisted Predictive Learning Architecture Supporting Quantum-Inspired Procedures for Live Power Trading Operations

The energy market is changing quickly because of the use of renewable energy sources, decentralized grids, and real-time demand response. These changes make things more complicated, like the need for quick decisions, uncertainty, and volatility in energy trading. Traditional computational models frequently inadequately address the scale and velocity necessary for optimal market performance. Quantum computing presents a substantial opportunity to transform the process of making market decisions. This paper examines the application of computer-assisted quantum algorithms for real-time trading in energy markets. Quantum-enhanced optimization, portfolio balancing, and real-time bidding strategies can be made by using both quantum computing and classical computing infrastructure. Adding quantum computing to energy trading will make calculations faster, predictions more accurate, and the economy more efficient. This will make energy markets smarter, greener, and more responsive.