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Advances in computational intelligence and robotics book series

Collective Dynamics of “Small-World” Networks Enhanced by Quantum Technology for Trusted AI Transactions

A. Sangeerani Devi, C. Saffina, Kiran Sree Pokkuluri, Shraddha Viraj Pandit, M. V. Rama Prasad, Goutam Tanty

Published Aug 2, 2024• 1 citation
Publisher

Abstract

Based on this research, the authors think that quantum technologies could be used to make small-world networks work better so that AI transactions can be trusted. The goal is to build small-world networks powered by quantum mechanics to reduce the risks that come from bad actors and people who don't have permission to join them. This will make it possible for AI agents to converse and do business safely. The ability of AI agents to carry out deals that can be trusted and checked is looked into in terms of the new features of quantum-enhanced small-world networks. The authors also look into how scalable quantum-enhanced small-world networks can be so that AI communities stay safe and reliable. They also look at possible uses for the proposed framework, such as in autonomous systems, healthcare, and the financial sector—all of which rely on AI agents being able to interact reliably and safely with each other. Finally, the authors talk about some of the problems and possible future research directions that come up with creating quantum-enhanced small-world networks that allow global trusted AI transactions.

DOI
10.4018/979-8-3693-5832-0.ch009

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Based on this research, the authors think that quantum technologies could be used to make small-world networks work better so that AI transactions can be trusted. The goal is to build small-world networks powered by quantum mechanics to reduce the risks that come from bad actors and people who don't have permission to join them. This will make it possible for AI agents to converse and do business safely. The ability of AI agents to carry out deals that can be trusted and checked is looked into in terms of the new features of quantum-enhanced small-world networks. The authors also look into how scalable quantum-enhanced small-world networks can be so that AI communities stay safe and reliable. They also look at possible uses for the proposed framework, such as in autonomous systems, healthcare, and the financial sector—all of which rely on AI agents being able to interact reliably and safely with each other. Finally, the authors talk about some of the problems and possible future research directions that come up with creating quantum-enhanced small-world networks that allow global trusted AI transactions.
To create trustworthy artificial intelligence transactions, this research investigates the collaborative dynamics of small-world networks that are supported by quantum technology. Small-world networks are well-known for their efficiency in the dissemination of information; nevertheless, concerns regarding security and responsibility impede their ability to operate on a large scale in artificial intelligence transactions. Utilizing a significant quantity of technology provides a solution to these difficulties. A secure environment for artificial intelligence transactions is provided by quantum-enhanced small-world networks, which demonstrate increased resilience against hostile attacks. Through the utilization of quantum trap and superposition, these networks facilitate the formation of trust among AI agents, hence enhancing the integrity and trustworthiness of transactions. The purpose of this investigation is to demonstrate the efficacy of quantum-enhanced small-world networks in encouraging confidence in artificial intelligence transactions.
Quantum networks, on the other hand, might help make hospital AI systems safer. Principles from the field of quantum physics are used by quantum networks to create communication channels that can't be changed or listened to. In this research, how healthcare workers are using quantum networks to make AI systems safer is looked into. The research talks about the main ideas behind quantum cryptography, such as quantum entanglement, quantum teleportation, and QKD methods. Integration issues and practical problems that come up when adding quantum networks to healthcare systems that are already in place are also talked about. When healthcare groups use quantum networks, they can make their defences stronger against cyber threats and data breaches. This will make people believe AI-powered healthcare solutions even more. In a world where healthcare is becoming more and more digital, our research adds to the ongoing conversation about cyber security in healthcare by suggesting quantum networks as a useful and effective way to keep private and sensitive patient data safe.
Quantum information processing depends on entanglement, which is very important for safe communication and more computer power. For reliable and scalable quantum transmission, decentralized quantum networks need to make sure that entanglement links are spread out and maintained well. In addition, our way can improve future efforts to distribute entanglement by using machine learning to look at how networks have interacted in the past. Concerns and possibilities of using AI-enhanced entanglement distribution systems on real quantum networks are also looked at. Our method combines artificial intelligence with quantum technologies to come up with new and exciting ways to solve problems and get the most out of quantum computing and communication in distributed systems. Ultimately, the strategic incorporation of AI with quantum computing in decentralized networks, in conjunction with the strategic utilization of artificial intelligence to enhance and regulate the allocation of entanglements in quantum networks, has the potential to bring about substantial disruption.
This paper explores the intersection of quantum computing and agentic AI by examining how quantum technologies can enhance the capabilities of autonomous agents, and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum-agentic platforms. Our work introduces a formal definition of quantum agents and outlines potential architectures that integrate quantum computing with agent-based systems. As a proof-of-concept, we develop and evaluate three quantum agent prototypes that demonstrate the feasibility of our proposed framework. Furthermore, we discuss use cases from both perspectives, including quantum-enhanced decision-making, quantum planning and optimization, and AI-driven orchestration of quantum workflows. By bridging these fields, we aim to chart a path toward scalable, intelligent, and adaptive quantum-agentic ecosystems.
There is a lot of talk about how the cyber world will change and perceive itself as quantum computing takes center stage in the computing industry in a decade. While some are enthusiastic about the advancement of quantum technology, others remain skeptical about its application in cyberspace. But there is no doubt the interaction of artificial intelligence (AI) and quantum computing offers cybersecurity both tremendous benefits and difficulties as we navigate the digital terrain. This article explores the complex interrelationship between these two state-of-the-art technologies and how it affects information system security. In this article, we look at the possible risks that the fusion of AI and quantum computing could bring, as well as the creative ways that experts are attempting to strengthen our cyber protections, current research and future directions.
The realm of quantum computing is experiencing rapid growth, harnessing the principles of quantum mechanics to exponentially accelerate computations that surpass classical computing capabilities. Such advancements can potentially reshape the landscape of reliable artificial intelligence, particularly in the context of data-intensive, intricate decision-making processes. Diverse trust-oriented AI frameworks have been introduced to address various AI applications. This paper evaluates multiple AI systems focused on trust, compiling observations about their corresponding quantum algorithms. The analysis delves into quantum algorithmic approaches within three key trust-centric AI areas: detecting fraudulent users in social networks, creating diagnostic systems for healthcare, and optimising pathways for trust aggregation in social networks. This study unveils a pivotal observation: quantum algorithms demonstrate diminished time complexity, enhancing trust-based AI applications' expeditiousness.