Quantum Artificial Intelligence (Quantum AI) stands at the convergence of quantum computing and machine learning, offering unprecedented potential for solving complex optimization problems that are intractable for classical systems. At its core, quantum machine learning capitalizes on the ability of qubits to represent data in high-dimensionality spaces, and store information exponentially more efficiently than classical bits. In this presentation, I will outline how Quantum AI and optimization are transforming across diverse fields, from portfolio optimization in finance to healthcare applications like medical image analysis and drug discovery.

I will explore the fusion of Federated Learning (FL) and Quantum AI, presenting a new paradigm for privacy-preserving collaborative learning across industries. First, from a model perspective, I will present Quantum Federated Learning framework, designed to handle unbalanced data distributions across healthcare institutions, significantly reduces communication rounds and improves classification performance, outperforming classical methods on medical image datasets and drug discovery tasks. Second, from an optimization perspective, I will discuss the critical challenge of computing gradients on quantum systems. Drawing inspiration from quantum natural gradients, I will establish a connection between quantum optimization and federated learning, demonstrating how this fusion can significantly enhance model performance and privacy preservation across quantum models.

Through these findings, I aim to advance Quantum AI and optimization techniques to tackle key challenges in collaborative learning, including safety, efficiency, robustness against adversarial attacks, and security. Following initial success, the work expanded into pathology, genomics, and medical and financial applications. Lastly, this talk will conclude with a discussion of the open challenges and future directions in quantum AI and optimization. Looking ahead, I will discuss scaling distributed Quantum AI, particularly focusing on leveraging photons, atomic memories or superconductive circuits to establish a distributed cloud Quantum Computer, which could surpass the capabilities of isolated quantum computers, unlocking new possibilities for complex computations while ensuring security and advancing quantum communication.

Dr. Amandeep Singh Bhatia is a Senior Postdoctoral Research Associate at North Carolina State University, working in the Department of Electrical & Computer Engineering under Kais’ Lab. His research lies at the intersection of quantum and classical computation, focusing on quantum AI, optimization algorithms, machine learning for quantum applications, privacy-preserving federated learning, tensor networks and efficient quantum system simulation. He applies these techniques across diverse industrial sectors, including healthcare, finance, and pharmaceuticals, with a particular emphasis on scaling Quantum AI for heterogeneous data in real-world, application-driven use cases. As a graduate student, he received a National Research Fellowship, was recognized as an IBM Quantum Advocate and Certified Quantum Practitioner, and served on the program committees of several leading quantum computing conferences. Before joining NC State, Dr. Bhatia was a Postdoctoral Researcher at Purdue University's School of Electrical & Computer Engineering. He also conducted research at the Institute for Theoretical Physics at the University of Tübingen, Germany, where he focused on developing quantum algorithms for optimization in financial applications.