Relevant bibliographies by topics / Quantum Reinforcement Learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Quantum Reinforcement Learning'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Quantum Reinforcement Learning"

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Daoyi Dong, Chunlin Chen, Hanxiong Li, and Tzyh-Jong Tarn. "Quantum Reinforcement Learning." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 5 (2008): 1207–20. http://dx.doi.org/10.1109/tsmcb.2008.925743.

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Lamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics." Photonics 8, no. 2 (2021): 33. http://dx.doi.org/10.3390/photonics8020033.

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Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum c
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Martín-Guerrero, José D., and Lucas Lamata. "Reinforcement Learning and Physics." Applied Sciences 11, no. 18 (2021): 8589. http://dx.doi.org/10.3390/app11188589.

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Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of
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Lockwood, Owen, and Mei Si. "Reinforcement Learning with Quantum Variational Circuit." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (2020): 245–51. http://dx.doi.org/10.1609/aiide.v16i1.7437.

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The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present
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Chen, Samuel Yen-Chi, Chih-Min Huang, Chia-Wei Hsing, Hsi-Sheng Goan, and Ying-Jer Kao. "Variational quantum reinforcement learning via evolutionary optimization." Machine Learning: Science and Technology 3, no. 1 (2022): 015025. http://dx.doi.org/10.1088/2632-2153/ac4559.

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Abstract Recent advances in classical reinforcement learning (RL) and quantum computation point to a promising direction for performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here, we present two frameworks for deep quantum RL tasks using gradient-free evolutionary optimization. First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using amplitude encoding. Second, we propose a hybrid framework where the quant
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Crawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines." Quantum Information and Computation 18, no. 1&2 (2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.

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We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations
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Yun, Won Joon, Jihong Park, and Joongheon Kim. "Quantum Multi-Agent Meta Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11087–95. http://dx.doi.org/10.1609/aaai.v37i9.26313.

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Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-design multi-agent reinforcement learning (MARL) based on the unique characteristics of quantum neural networks (QNNs) having two separate dimensions of trainable parameters: angle parameters affecting the output qubit states, and pole parameters associated with the output measurement basis. Exploiting this dyadic trainability as meta-learning capabilit
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Wu, Shaojun, Shan Jin, Dingding Wen, Donghong Han, and Xiaoting Wang. "Quantum reinforcement learning in continuous action space." Quantum 9 (March 12, 2025): 1660. https://doi.org/10.22331/q-2025-03-12-1660.

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Quantum reinforcement learning (QRL) is a promising paradigm for near-term quantum devices. While existing QRL methods have shown success in discrete action spaces, extending these techniques to continuous domains is challenging due to the curse of dimensionality introduced by discretization. To overcome this limitation, we introduce a quantum Deep Deterministic Policy Gradient (DDPG) algorithm that efficiently addresses both classical and quantum sequential decision problems in continuous action spaces. Moreover, our approach facilitates single-shot quantum state generation: a one-time optimi
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Pasupuleti, Murali Krishna. "Intelligent Quantum Control Systems Based on Superalgebraic Hamiltonians." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 76–90. https://doi.org/10.62311/nesx/rp0625.

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This paper presents a novel framework for intelligent quantum control that integrates deep reinforcement learning with the algebraic rigor of superalgebraic Hamiltonians derived from Lie superalgebras such as osp(1∣2) and sl(2∣1). Traditional quantum control approaches often fail to respect the rich internal symmetries present in supersymmetric systems, leading to suboptimal or physically inconsistent strategies. To address this, we design a structure-aware control system that embeds symbolic algebraic constraints—such as graded commutation relations and Casimir invariants—directly into the le
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Andrés, Eva, Manuel Pegalajar Cuéllar, and Gabriel Navarro. "Brain-Inspired Agents for Quantum Reinforcement Learning." Mathematics 12, no. 8 (2024): 1230. http://dx.doi.org/10.3390/math12081230.

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In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates quan
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Dissertations / Theses on the topic "Quantum Reinforcement Learning"

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Nuuman, Sinan. "Quantum reinforcement learning for dynamic spectrum access in cognitive radio networks." Thesis, University of York, 2016. http://etheses.whiterose.ac.uk/15617/.

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This thesis proposes Quantum Reinforcement Learning (QRL) as an improvement to conventional reinforcement learning-based dynamic spectrum access used within cognitive radio networks. The aim is to overcome the slow convergence problem associated with exploration within reinforcement learning schemes. A literature review for the background of the carried out research work is illustrated. Review of research works on learning-based assignment techniques as well as quantum search techniques is provided. Modelling of three traditional dynamic channel assignment techniques is illustrated and the adv
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Teixeira, Miguel Alexandre Brandão. "Quantum Reinforcement Learning applied to Games." Master's thesis, 2021. https://hdl.handle.net/10216/135628.

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Teixeira, Miguel Alexandre Brandão. "Quantum Reinforcement Learning applied to Games." Dissertação, 2021. https://hdl.handle.net/10216/135628.

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Books on the topic "Quantum Reinforcement Learning"

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Kunczik, Leonhard. Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37616-1.

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Kunczik, Leonhard. Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Fachmedien Wiesbaden GmbH, 2022.

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Heng, Liao, and Bill McColl, eds. Mathematics for Future Computing and Communications. Cambridge University Press, 2021. http://dx.doi.org/10.1017/9781009070218.

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For 80 years, mathematics has driven fundamental innovation in computing and communications. This timely book provides a panorama of some recent ideas in mathematics and how they will drive continued innovation in computing, communications and AI in the coming years. It provides a unique insight into how the new techniques that are being developed can be used to provide theoretical foundations for technological progress, just as mathematics was used in earlier times by Turing, von Neumann, Shannon and others. Edited by leading researchers in the field, chapters cover the application of new mat
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Busemeyer, Jerome R., Zheng Wang, James T. Townsend, and Ami Eidels, eds. The Oxford Handbook of Computational and Mathematical Psychology. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.001.0001.

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A comprehensive and authoritative review on most important developments in computational and mathematical psychology that have impacted many other fields in past decades. Written in tutorial style by leading scientists in each topic area, with an emphasis on examples and applications. Each chapter is self-contained and aims to engage readers with various levels of modeling experience. The Handbook covers the key developments in elementary cognitive mechanisms (e.g., signal detection, information processing, reinforcement learning), basic cognitive skills (e.g., perceptual judgment, categorizat
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Book chapters on the topic "Quantum Reinforcement Learning"

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Dong, Daoyi, Chunlin Chen, and Zonghai Chen. "Quantum Reinforcement Learning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539117_97.

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Kunczik, Leonhard. "Quantum Reinforcement Learning—Connecting Reinforcement Learning and Quantum Computing." In Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37616-1_4.

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Rajagopal, K., Q. Zhang, S. N. Balakrishnan, P. Fakhari, and J. R. Busemeyer. "Quantum Amplitude Amplification for Reinforcement Learning." In Handbook of Reinforcement Learning and Control. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60990-0_26.

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Seetohul, Ved, Hamid Jahankhani, Stefan Kendzierskyj, and Isuru Sandakelum Will Arachchige. "Quantum Reinforcement Learning: Advancing AI Agents Through Quantum Computing." In Space Law and Policy. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64045-2_4.

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Kunczik, Leonhard. "Evaluating Quantum REINFORCE on IBM’s Quantum Hardware." In Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37616-1_8.

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Mohan, Arjun, Sudharsan Jayabalan, and Archana Mohan. "Autonomous Quantum Reinforcement Learning for Robot Navigation." In Proceedings of 2nd International Conference on Intelligent Computing and Applications. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1645-5_29.

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Kim, Joongheon. "Quantum Reinforcement Learning: Concepts, Models, and Applications." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75593-4_1.

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Kunczik, Leonhard. "Approximation in Quantum Computing." In Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37616-1_5.

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Neumann, Niels M. P., Paolo B. U. L. de Heer, Irina Chiscop, and Frank Phillipson. "Multi-agent Reinforcement Learning Using Simulated Quantum Annealing." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50433-5_43.

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Kunczik, Leonhard. "Reinforcement Learning and Bellman’s Principle of Optimality." In Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37616-1_3.

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Conference papers on the topic "Quantum Reinforcement Learning"

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Kruse, Georg, Rodrigo Coelho, Andreas Rosskopf, Robert Wille, and Jeanette-Miriam Lorenz. "Benchmarking Quantum Reinforcement Learning." In Workshop on Quantum Artificial Intelligence and Optimization 2025. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013393200003890.

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Kim, Gyu Seon, Soohyun Park, and Joongheon Kim. "Quantum Reinforcement Learning: An Overview." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10826656.

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Mpofu, K. T., and P. Mthunzi-Kufa. "Parametrized Quantum Circuits for Reinforcement Learning." In 2024 4th International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE, 2024. https://doi.org/10.1109/imitec60221.2024.10850927.

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Meyer, Jan, Kay Glatting, Sigurd Huber, and Gerhard Krieger. "Quantum Reinforcement Learning for Cognitive SAR." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642330.

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Eisenmann, Simon, Daniel Hein, Steffen Udluft, and Thomas A. Runkler. "Model-Based Offline Quantum Reinforcement Learning." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.00175.

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Muscalagiu, Anca-Ioana. "Quantum Neural Network Design via Quantum Deep Reinforcement Learning." In 16th International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012997500003837.

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Chen, Samuel Yen-Chi. "Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.00178.

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Liu, Chen-Yu, Chu-Hsuan Abraham Lin, Chao-Han Huck Yang, Kuan-Cheng Chen, and Min-Hsiu Hsieh. "QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10299.

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Park, Junghoon, Jiook Cha, Samuel Yen-Chi Chen, Shinjae Yoo, and Huan-Hsin Tseng. "Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforcement Learning." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.00185.

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Braniff, Austin, Fengqi You, and Yuhe Tian. "Enhanced Reinforcement Learning-driven Process Design via Quantum Machine Learning." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.149501.

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In this work, we introduce a quantum-enhanced reinforcement learning (RL) framework for process design synthesis. RL-driven methods for generating process designs have gained momentum due to their ability to intelligently identify optimal configurations without requiring pre-defined superstructures or flowsheet configurations. This eliminates reliance on prior expert knowledge, offering a comprehensive and robust design strategy. However, navigating the vast combinatorial design space poses computational challenges. To address this, a novel approach integrating RL with quantum machine learning
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Reports on the topic "Quantum Reinforcement Learning"

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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Guy, Khalil, and Gabriel Perdue. Using Reinforcement Learning to Optimize Quantum Circuits in thePresence of Noise. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1661681.

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Pasupuleti, Murali Krishna. AI in Global Strategy: Harnessing Game Theory and Reinforcement Learning for Diplomatic Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rr125.

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Abstract: This article explores the integration of game theory and reinforcement learning (RL) in the context of global diplomacy, emphasizing the transformative potential of strategic AI in international relations. It provides an in-depth analysis of how game theory principles, such as Nash equilibrium and cooperative strategies, are leveraged by AI to model and optimize diplomatic interactions. The Article explains how reinforcement learning enables AI systems to learn and adapt strategies over time, improving their effectiveness in negotiation and conflict resolution. Through case studies a
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Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.

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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and
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Pasupuleti, Murali Krishna. Smart Nanomaterials and AI-Integrated Grids for Sustainable Renewable Energy. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1025.

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Abstract: The transition to sustainable and intelligent renewable energy systems is being driven by advancements in smart nanomaterials and AI-integrated smart grids. Nanotechnology has enabled the development of high-performance energy materials, such as graphene, perovskites, quantum dots, and MXenes, which enhance the efficiency, durability, and scalability of renewable energy solutions. Simultaneously, AI-driven smart grids leverage machine learning, deep learning, and digital twins to optimize energy distribution, predictive maintenance, and real-time load balancing in renewable energy ne
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