We evaluate how quantum computing can be applied to security problems for software agents. Agent-based computing, which merges technological advances in artificial intelligence and mobile computing, is a rapidly growing domain, especially in applications such as electronic commerce, network management, information retrieval, and mission planning. System security is one of the more eminent research areas in agent-based computing, and the specific problem of protecting a mobile agent from a potentially hostile host is one of the most difficult of these challenges. In this work, we describe our agent model, and discuss the capabilities and limitations of classical solutions to the malicious host problem. Quantum computing may be extremely helpful in addressing the limitations of classical solutions to this problem. This paper highlights some of the areas where quantum computing could be applied to agent security.
We evaluate how quantum computing can be applied to security problems for software agents. Agent-based computing, which merges technological advances in artificial intelligence and mobile computing, is a rapidly growing domain, especially in applications such as electronic commerce, network management, information retrieval, and mission planning. System security is one of the more eminent research areas in agent-based computing, and the specific problem of protecting a mobile agent from a potentially hostile host is one of the most difficult of these challenges. In this work, we describe our agent model, and discuss the capabilities and limitations of classical solutions to the malicious host problem. Quantum computing may be extremely helpful in addressing the limitations of classical solutions to this problem. This paper highlights some of the areas where quantum computing could be applied to agent security.
Agentic artificial intelligence (AI) is a type of AI in which machines act like intelligent agents. These agents can observe their surroundings, learn from experience, think, and make decisions to achieve a goal. As AI agents are used in more complex tasks, normal computers often find it difficult to process large amounts of information quickly. Quantum computing is a new kind of computing that uses special rules of physics to solve certain problems faster than traditional computers. This chapter explains how quantum computing can act as a catalyst, or a powerful helper, for improving agentic AI. It first introduces the basic ideas of agentic AI and AI agents and then explains the key concepts of quantum computing in a simple way. The chapter shows how quantum computing can help AI agents learn faster, make better decisions, and find better solutions. It also discusses real-world applications, current limitations of quantum technology, and future possibilities. Overall, this chapter highlights how quantum computing can play an important role in building smarter and more capable AI agents.
The fundamental aim in the age of information technology (IT) and artificial intelligence (AI) is to ensure software or web application security. A security alternative provides a wide range of software security approaches and algorithms. To improve the security character, security evaluation is an important component for assessment, management, and control. Early security evaluation assists in distinguishing worms, hazards, weaknesses, and threats. We are aware of that. The current paper will discuss how quantum computing is defined and characterised in software security. We utilise many techniques or algorithms to safeguard our financial organisations, our medical equipment, our military weaponry, our aviation systems, our boats, vehicles, navigators, etc. in software security (cryptography is the science or art to build a data protocol). However, when the big quantum computer develops, many cryptosystems will certainly fail. The current encryption method is becoming outdated as the cryptographic technique may be resolved by quantum computers. Therefore, in the framework of the existing quantum safety, it is necessary to focus more on intensive study. The key challenges in the quantum age would be cryptography solutions that satisfy the need for security, usability, and adaptability without jeopardising user trust. This study focused on Grover’s work and how the security aspect of software may be improved. In the field of software and online applications, software security in quantum computing will be investigated, given the importance of security issues in the quantum computing age.
Quantum computing has emerged as a transformative computing paradigm capable of solving problems that remain computationally infeasible for classical systems; however, its rapid advancement also introduces significant security, privacy, and reliability concerns. In this context, this survey presents a comprehensive review of security challenges and mitigation strategies associated with quantum computing, focusing on security issues in quantum computers, security threats caused by quantum computers, and security mechanisms developed for quantum systems. The paper examines vulnerabilities in quantum hardware and software, the impact of quantum computing on existing cryptographic infrastructures and cybersecurity mechanisms, and the development of quantum-resilient solutions such as post-quantum cryptography, quantum-safe communication protocols, quantum intrusion detection systems, and quantum-aware software engineering techniques. In addition, the survey discusses emerging applications of quantum technologies in cybersecurity domains, including malware detection, network intrusion detection, Internet of Things (IoT) security, and secure communication systems. Furthermore, the paper analyzes existing quantum error mitigation and fault-tolerance approaches designed to improve the robustness and trustworthiness of quantum computation under realistic noisy conditions. By consolidating recent advances, open research challenges, and future directions, this survey provides a structured overview of the evolving intersection between quantum computing and cybersecurity, while serving as a reference for researchers and practitioners working toward secure, resilient, and quantum-ready computing infrastructures.
Cybersecurity is crucial and has a worldwide significant impact in several industries that must be addressed to preserve, protect, and uphold the integrity of any system. Quantum artificial intelligence is a combination of quantum computing and machine learning techniques. It can be used for various cybersecurity applications that require advanced methodologies for cyberattack detection and prevention. Nowadays, traditional computers are reaching their limits and are becoming less effective against advanced attacks, which enables further requirements of quantum-based algorithms. The computational power of quantum computing can greatly reduce cybersecurity threats by facilitating advanced threat detection, rapid information processing, and automation of safety protocols that are extremely difficult to do with classical computing techniques. This chapter outlines the approach, future scope, hurdles, and areas of implementing quantum advancements to make our cyber defense system more sophisticated against cyber threats powered by quantum computers.
Quantum computing is a rapidly developing technology that uses the principles of quantum mechanics to perform information processing tasks that are beyond the capabilities of traditional computers. Quantum computers employ qubits, superposition, and entanglement to rapidly resolve intricate problems, with potential applications in cryptography, artificial intelligence, the development of new medications, and financial modeling. Recent breakthroughs have shown potential, but hurdles like correcting errors, maintaining qubit stability, expanding and the expense of substantial infrastructure continue to hinder widespread usage. Quantum computing also sparks ethical and security concerns, especially in relation to encryption and the development of artificial intelligence. Despite some challenges, continuous research and development are gradually enhancing the capabilities of quantum hardware and algorithms. Advances in quantum technology are anticipated to transform various sectors and scientific inquiry, providing answers to several of the globe's most intricate problems. To maximize the potential of this initiative, additional investment in education, policy refinement, and judicious implementation is essential to guarantee a balanced and positive influence on society.
With the rapid advancement of artificial intelligence (AI), a new paradigm is evolving that includes agentic AI systems capable of autonomously reasoning, planning, and executing multi-step actions. This chapter provides a detailed overview of agentic AI, AI agents, and the developing area of quantum AI agents – systems that utilize the capabilities of quantum computers to exceed the limits imposed by classical computer architecture. In addition to analyzing the architectural foundations of contemporary AI agents such as large language model (LLM)-based agents, multi-agent systems, and hybrid classical–quantum architectures, this chapter presents a unified theoretical framework for understanding how the application of quantum enhancement could result in exponential improvements in optimization, simulation, and pattern recognition abilities of agents using recent advances in quantum machine learning, variational quantum algorithms, and quantum neural networks. Furthermore, this chapter examines transformative applications across healthcare, finance, scientific discovery, and autonomous systems while identifying the current limitations of quantum AI, including decoherence, qubit counts, and alignment challenges. Finally, this chapter concludes that the intersection of agentic AI and quantum computing is likely one of the most significant technological developments of the 21st century, requiring sustained research, ethical frameworks, and inter-disciplinary cooperation.