Note: The Website has moved - so go to https://engineering.purdue.edu/IE/PurdueQuantumAIWorkshop2025 for updated information. 

Quantum computing has emerged as a groundbreaking paradigm, offering the promise of unparalleled speed and efficiency in solving complex problems. This transformative technology has spurred rapid advancements in the fields of quantum artificial intelligence (Quantum AI) and beyond. By harnessing the principles of quantum mechanics, Quantum AI holds the potential to revolutionize disciplines such as machine learning, cryptography, optimization, and communication systems, paving the way for innovations that were once considered out of reach. 

This symposium aims to delve into the forefront of Quantum AI research, addressing key questions and exploring emerging possibilities. What novel capabilities does Quantum AI unlock, and how can they enhance the performance of systems reliant on data-driven insights? How will these advancements shape the future of autonomy, resilience, and operational efficiency across industries? Moreover, what challenges must be overcome to translate theoretical breakthroughs into practical applications? From algorithm design to hardware scalability, this field offers vast opportunities for innovation and significant hurdles to address. 

Participants in this symposium will engage with cutting-edge research, network with experts, and explore new business models and use cases for Quantum AI in practice. Together, we will investigate how this technology can be leveraged to redefine the boundaries of what is computationally possible while fostering a deeper understanding of its implications for industry and society. Whether you are an academic, a practitioner, or an enthusiast, this event provides a unique platform to contribute to and learn from the rapidly evolving Quantum AI landscape.

This is the second event, where the information of the first event can be seen at NSF Workshop on Post Quantum AI.  

 This symposium will cover a wide range of topics at the intersection of quantum computing and artificial intelligence, including but not limited to:

• Quantum Speedup for AI Algorithms: Harnessing quadratic and exponential speedups for optimization, search, and learning tasks.

• Quantum Annealing and Combinatorial Optimization: Exploring applications of quantum annealers in solving NP-hard problems and industry-specific use cases.

• Quantum Generative AI: Developing and analyzing quantum-enhanced generative models for data synthesis, creativity, and simulation.

• Hybrid Quantum-Classical Architectures: Designing systems that leverage the strengths of both classical and quantum computation for scalable AI solutions.

• Quantum Neural Networks (QNNs): Advancing the design and application of neural networks operating in the quantum regime.

• Quantum Computing in Natural Language Processing (NLP): Leveraging quantum algorithms to enhance language understanding and semantic analysis.

• Quantum Cryptography and Secure AI Models: Addressing the challenges of integrating quantum-based security measures in AI systems.

• Error Mitigation and Fault Tolerance in Quantum AI: Developing methods to ensure reliability and scalability of quantum computations.

• Quantum Hardware for AI: Investigating advancements in qubit technologies and their implications for AI workloads.

• Ethics and Societal Impact of Quantum AI: Analyzing the broader implications of quantum technologies on society, ethics, and policy.

• Business and Industry Applications of Quantum AI: Exploring viable models for deploying quantum-enhanced AI in sectors like finance, healthcare, logistics, and telecommunications.

• Role of Quantum AI in Augmenting Human Work: Improving productivity, creativity, and workplace ergonomics with Quantum AI.

• Quantum Transformation in Industrial Engineering: Transitioning industrial practices to leverage quantum paradigms.

• Future of Education: Reshaping curricula and research directions to integrate Quantum AI advancements.

Gavriel Salvendy International Symposia on Frontiers in Industrial Engineering committee

Fan Chen, Indiana University 

Sebastian Feld, Delft University of Technology

Rebekah Herrman, University of Tennessee Knoxville

Mohsen Heidari, Indiana University 

Luigi Iapichino, Leibniz Supercomputing Centre

Pooyan Jamshidi, University of South Carolina

Mohammad Ali Javidian, Appalachian State University

Debanjan Konar, Samsung

Dongyang Li, Purdue

Dheeraj Peddireddy, Purdue University

Shaswot Shresthamali, Kyushu University

Teague Tomesh, Infleqtion

Takao Tomono, Keio University

Steffen Udluft, Siemens Technology

Xin Wang, Baidu

Junpeng Zhan, Alfred University

Ruizhe Zhang, Simons Institute for the Theory of Computing at UC Berkeley

Jianjun Zhao, Kyushu University

Shuchen Zhu, Duke University

Yijie Zhu, Lancaster University