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 (QC) and AI is a potentially effective remedy for the existing energy crisis. The present analysis explores the theoretical underpinnings of quantum AI 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 subatomic 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 QC and AI 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 AI 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: 20 July 2023 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Luka Baklaga: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization.
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 (QC) and AI is a potentially effective remedy for the existing energy crisis. The present analysis explores the theoretical underpinnings of quantum AI 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 subatomic 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 QC and AI 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 AI 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: 20 July 2023 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Luka Baklaga: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization.
Quantum computing, artificial intelligence (AI), and robots are all coming together to make sustainable agriculture possible by making it possible to build advanced self-driving systems for precision farming. This paper looks at how quantum-enhanced AI might be able to make farming much more productive, use resources more efficiently, and protect the environment. In traditional farming, old techniques are often still used, which can waste resources and hurt the environment. By combining quantum computing with AI and robotics, we can use the power of quantum algorithms to process huge amounts of agricultural data faster than with traditional systems. This makes it possible to create smart decision-making tools that increase crop yields while lowering costs and damage to the environment. Quantum computing has unmatched computer power, which makes it possible to analyze large files from a variety of sources, such as earth monitors, weather forecasts, and satellite images. Because of this feature, complex prediction models can be made that can accurately predict how crops will do in different situations. The paper study used suggested model along with AI to handle important farming jobs like planting, watering, and gathering by using robots with advanced sensors and machine learning algorithms. With the use of self-driving robots in farming not only makes operations more efficient, but it also cuts down on the need for physical labor, which helps the agricultural sector deal with its lack of workers.
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.
Because of new technology and machine learning, 2022 became a milestone year in computing history. Such developments have led to a new revolution, which will help address some of the most challenging issues our environment faces. Quantum artificial intelligence (QAI) is able to deal with information and complete computations that are beyond the abilities of traditional systems because it uses ideas unique to quantum mechanics, such as superposition and entanglement. QAI can be proven highly effective in addressing difficult challenges related to resource optimization, the behavior of ecosystems, climate problems, and risks to the environment in environmental management. Using the latest quantum algorithms and parallel computing, QAI can precisely simulate and examine large environmental systems. Because of this, the predictions of environmental phenomena are improved, helping people make more informed decisions toward sustainability. QAI is used in important ways for climate modelling. What distinguishes quantum computers from classical ones is their ability to model atmospheric and oceanic processes in much greater detail. The technology in this field can change how well we predict the effects of climate change. Because of this, global efforts can be better directed to prevent and adapt to the possible effects of climate change. In addition, QAI might significantly change how we think about ecosystems. By relying on quantum neural networks and machine learning, QAI analyzes large volumes of ecological data and discovers connections and relationships that explain what happens inside ecosystems. Because of these insights, we can guide restoration efforts, support conservation initiatives, and ensure sustainable ways of using natural resources to safeguard the balance of nature. The use of QAI also helps in making fair decisions on resource allocation and in lowering environmental impact. Powerful quantum algorithms solve difficult optimization problems much faster, which helps to protect the environment. These algorithms might be applied to help the environment by enhancing how companies move goods, reducing fuel consumption and associated emissions, and increasing the use of renewable energy sources. Ongoing studies are aimed at uncovering every benefit from this technology, despite the fact that QAI for environmental management is still new. These efforts focus on making reliable quantum algorithms, enhancing methods to fix errors in quantum systems, and creating new quantum hardware. People working in environmental science, quantum technology, and legal sectors will need to come together and cooperate to apply theoretical ideas to everyday solutions for the environment. Taking this cooperative strategy will help QAI contribute to the goal of ensuring a healthy and safe Earth for generations to come.
Quantum Green Computing represents a transformative paradigm that merges the power of quantum computational techniques with the principles of sustainability to drive innovation in renewable resource management.This paper investigates the interplay between quantum algorithms, energy-efficient system architectures, and advanced renewable energy technologies-including solar photovoltaics, wind energy systems, and bioenergy solutions.By leveraging the unique capabilities of quantum computing, such as superior optimization, high-dimensional modeling, and rapid problem-solving, the study explores novel strategies for addressing the inherent complexities of renewable energy integration.The research emphasizes three core dimensions: (1) the application of quantum optimization algorithms to enhance grid stability, load balancing, and real-time decision-making; (2) the development of energy-efficient quantum-inspired hardware architectures that align with green computing objectives; and (3) the formulation of predictive and adaptive models for renewable resource allocation using quantum-inspired approaches.Furthermore, the paper highlights case studies that demonstrate how quantum techniques can significantly improve grid management, predictive maintenance of renewable infrastructures, and long-term forecasting of resource availability under dynamic environmental conditions.By critically evaluating these emerging pathways, the study outlines a vision where quantum-enabled sustainability not only reduces carbon emissions but also maximizes energy utilization efficiency.This integration holds the potential to revolutionize the global renewable energy landscape, enabling resilient, lowcarbon ecosystems capable of supporting the growing demands of a sustainable future.
The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the potential to revolutionize these critical areas. This review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development. Traditional computational methods often fall short in handling the scale and complexity of climate models and natural resource management. Quantum advancements, however, offer significant improvements in computational efficiency and problem-solving capabilities. By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi-infrastructure systems towards climate neutrality. The paper also evaluates the performance of current quantum algorithms and hardware in practical applications and presents realistic cases, i.e., waste-to-energy in anaerobic digestion, disaster prevention in flooding prediction, and new material development for carbon capture. The integration of these quantum technologies promises to drive significant advancements in achieving climate resilience and sustainable development.
The rapid advancement of Artificial Intelligence (AI) is reshaping industries and driving global innovation. However, the increasing complexity of AI models demands substantial data and computational resources, leading to significant energy consumption and environmental impact. This article explores the integration of quantum computing and end-to-end automation strategies in cloud-edge architectures. It proposes a hybrid quantum-classical AI framework that enhances training efficiency and reduces data and processing intensity by minimizing energy consumption. The framework leverages automated model orchestration, adaptive resource allocation, and intelligent data processing at the edge to improve system efficiency. In addition, it addresses ethical considerations, including privacy, fairness, and trustworthiness, to ensure alignment with human values. This approach significantly improves AI performance while fostering a sustainable and ethical AI ecosystem.