Revolutionizing Sustainable Energy Production with Quantum Artificial Intelligence: Applications in Autonomous Robotics and Data Management

Abstract

The present commentary delineates the scope of quantum-based artificial intelligence (AI) in confronting the escalating need for eco-friendly sources of energy. Contemporary concerns regarding the detrimental impacts of conventional energy sources on the environment have led to the exploration of alternative and sustainable options. The amalgamation of quantum computing and AI is a potentially effective remedy for the existing energy crisis. The present analysis explores the theoretical underpinnings of quantum artificial intelligence and its palpable ramifications in the energy realm. It highlights the capacity of quantum AI to enhance energy efficiency, reduce waste, and minimize the detrimental impact of energy production on the environment. Experimental techniques, such as solar irradiation, wind velocity, and pumped-water energy storage, were utilized to demonstrate the effectiveness of quantum AI in predicting renewable energy generation. Several information models and machine learning techniques have been examined to identify models that yield accurate predictions of renewable energy generation. The results demonstrate that quantum AI can easily perform complex quantum sub-atomic simulations, making it a valuable tool in the fields of computational science and molecular physics. A quantum AI-powered automated robotic system is proposed to optimize energy management. The implementation of a monitoring system that monitors energy consumption levels, mechanizes the production and distribution processes of energy, and anticipates future energy demands in real time, renders it an indispensable device for both energy conglomerates and policymakers. This study ascertains that the amalgamation of quantum computing and artificial intelligence harbors substantial potential for sustainable energy generation, marked by streamlined energy production, minimal waste generation, and ecoconservation. The present investigation furnishes insightful observations pertaining to the potential benefits of quantum artificial intelligence for the purpose of ensuring sustainable energy production. Additionally, it presents a roadmap for future research in this area.

Received: 22 January 2023 | Revised: 7 May 2023 | Accepted: 8 July 2023

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.