Agentic AI, AI Agents, and Quantum AI Agents

Ved Seetohul, Hamid Jahankhani, Stefan Kendzierskyj +1 more

Satish Kumar Satti, Kothandapani Suganya Devi, Naresh Babu Muppalaneni +1 more

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.

Yingzhao Zhu, Kefeng Yu

Lei Zhang, Mingrui Jing, Yusheng Zhao +2 more

AI agents are increasingly capable of constructing quantum algorithms and proofs, yet machine-checked proofs alone cannot guarantee that the intended problem is solved or that resource claims match the verified computation. Here we introduce Lean-QuantumAlg, a Lean 4 framework for trustworthy AI-assisted development of quantum algorithms. Its Algorithm-Solves-Problem contract binds correctness, success probability, borrowed-state restoration and quantum and classical resource bounds to a single execution, while separating the guarantees that Lean checks from the problem specifications and execution models that humans review. We demonstrate the framework on nine AI-formalized quantum algorithms, from Grover search to quantum attacks on RSA-2048 and P-256, with less than 1% of each algorithm's imported source code requiring statement-level human review once the shared framework has been audited. By integrating reusable mathematical foundations, certified algorithmic primitives and execution-grounded verification, Lean-QuantumAlg provides a basis for AI agents to construct and certify quantum algorithms, opening a path toward scalable AI-driven discovery of quantum algorithms.

Sanniva Halder Bapon, Amandeep Kaur

Hemant Palivela, Meera Narvekar

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.