On the integration of artificial intelligence with quantum sensing technologies for environmental monitoring, these make it much more precise, efficient and real time information end-user.In order to combat climate change markers, insulation effectiveness, and environmental hazard immediate response systems, this research dictates an AI based quantum sensing framework.Quantum augmented machine learning methods, i.e., Quantum Bayesian Networks and Quanvolutional Neural Networks, are used to improve accuracy of measurements from sensors and furthermore sensor calibration.In addition, the proposed system incorporates Q-SCALE for advanced sensor calibration to eliminate noise and improve sensor performance.The Quantum Wireless Sensing framework, in the form of quantum-based sensors, has an advantage in sensing dynamic environmental factors with an increased sensitivity.Some key findings include that AI quantum AI sensors are better than traditional sensors at alerting to abrupt environmental shifts, monitoring pollutants, testing greenhouse conditions, etc.Quantum entanglement was not used, and quantum sensors also achieved secure data transmission of distances beyond 50 kilometers.The proposed system achieves real-time data acquisition using lightweight models optimized for such resource constrained environments and reduces both the energy consumption.This major step in research will lead towards development of more accurate as well as scalable, sustainable environmental monitoring solutions with enhanced data security.As such, recent innovations such as AI coupled with nanomaterials and IoT-instigated quantum surveillance systems indicate the extent of this field's capability.
On the integration of artificial intelligence with quantum sensing technologies for environmental monitoring, these make it much more precise, efficient and real time information end-user.In order to combat climate change markers, insulation effectiveness, and environmental hazard immediate response systems, this research dictates an AI based quantum sensing framework.Quantum augmented machine learning methods, i.e., Quantum Bayesian Networks and Quanvolutional Neural Networks, are used to improve accuracy of measurements from sensors and furthermore sensor calibration.In addition, the proposed system incorporates Q-SCALE for advanced sensor calibration to eliminate noise and improve sensor performance.The Quantum Wireless Sensing framework, in the form of quantum-based sensors, has an advantage in sensing dynamic environmental factors with an increased sensitivity.Some key findings include that AI quantum AI sensors are better than traditional sensors at alerting to abrupt environmental shifts, monitoring pollutants, testing greenhouse conditions, etc.Quantum entanglement was not used, and quantum sensors also achieved secure data transmission of distances beyond 50 kilometers.The proposed system achieves real-time data acquisition using lightweight models optimized for such resource constrained environments and reduces both the energy consumption.This major step in research will lead towards development of more accurate as well as scalable, sustainable environmental monitoring solutions with enhanced data security.As such, recent innovations such as AI coupled with nanomaterials and IoT-instigated quantum surveillance systems indicate the extent of this field's capability.
Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. However, they both face numerous challenges on the path to practical application. Artificial intelligence (AI), as a major technological advancement in current scientific landscape, offers powerful data processing and analytical capabilities, providing new ideas and methods for optimizing and enhancing quantum communication and sensing systems. In quantum communication, AI techniques have greatly improved the performance and security of quantum key distribution, quantum memory, and quantum networks through parameter optimization, real-time feedback control, and attack detection. In quantum sensing, quantum sensing technology enables ultra-high sensitivity detection of physical quantities such as time and magnetic fields. The introduction of AI has opened up new avenues for achieving high-precision and high-sensitivity quantum measurements. With AI, sensor performance is optimized, and measurement accuracy is further enhanced through data analysis. This paper also analyzes the current challenges in using AI to empower quantum communication and sensing systems, such as implementing efficient algorithm deployment and system feedback control under limited computational resources, and addressing complex task environments, dynamically changing scenarios, and multi-task coordination requirements. Finally, this paper discusses and envisions future development prospects in this field.
Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.
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.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.
Environmental monitoring is essential for comprehending and addressing the consequences of climate change and pollution on our world. Conventional approaches to environmental monitoring and item detection have demonstrated efficacy, but frequently encounter drawbacks in terms of precision, immediate data collection, and resource utilization. This study investigates the potential of quantum networks to transform environmental monitoring by improving item detection skills. We performed a series of experiments to assess the effectiveness of our Quantum Network methodology in comparison to two established methodologies, Existing Method A and Existing Method B. The quantum network exhibited a notable enhancement in the precision of object detection, with an astounding accuracy of 95.6% in real-time data acquisition for air quality, temperature, and humidity measurements. The performance of this method exceeded the accuracies produced by the existing methods, which were 87.4% and 92.1% respectively. Although current approaches have been extensively used and provide established solutions for environmental monitoring, our quantum network demonstrated distinct benefits. It enhanced the precision of object detection, allowing for more exact gathering of data on crucial environmental characteristics. Furthermore, our methodology facilitated data collection in real time, rendering it exceptionally well-suited for applications that necessitate prompt adaptation to fluctuating environmental circumstances. Nevertheless, it is crucial to recognize that adopting quantum technology brings about intricacy and resource demands. The promise of the quantum network is contingent upon additional refinement and optimization to properly tackle these difficulties.
Sustainable supply chains are becoming increasingly important as businesses strive to reduce environmental impact while maintaining efficiency and profitability. One emerging solution is Quantum Intelligence (Quantum AI)—a powerful combination of quantum computing and artificial intelligence that can handle highly complex and data-intensive supply chain problems. Quantum AI leverages unique quantum principles such as superposition and entanglement to explore a vast number of possible solutions simultaneously. This enables better decision-making in areas like production scheduling, inventory management, and emissions forecasting. As a result, organizations can optimize delivery routes, reduce operational costs, and significantly lower carbon emissions, contributing to more sustainable and environmentally responsible supply chains. A key approach in this field is the hybrid quantum-classical model, which combines the data-processing strengths of classical computing with the advanced computational capabilities of quantum systems. This hybrid approach helps overcome current limitations in quantum hardware, such as noise, limited qubits, and high error rates, making Quantum AI more practical for real-world applications. In addition, integrating technologies like the Internet of Things (IoT), blockchain, and digital twins further enhances supply chain performance. IoT devices provide real-time data from sensors, blockchain ensures transparency and traceability, and digital twins allow simulation of supply chain operations to predict environmental impacts and test different scenarios. Overall, Quantum AI represents a promising step toward building smarter, more efficient, and sustainable supply chains that can adapt to modern challenges while supporting global environmental goals.