Our research group is at the forefront of integrating quantum computing and AI, advancing applications in smart energy systems, fault diagnosis, molecular design, and product development. These research efforts have been widely recognized by various media outlets.
Media Coverage
Our research has been featured in:
- Cornell Chronicle: Tying quantum computing to AI prompts a smarter power grid
- Cornell Engineering News: Quantum AI framework targets energy intensive data centers
- Tech Xplore / Phys.org: Tying quantum computing to AI prompts a smarter power grid
- HPCwire: Cornell Researchers Integrate Quantum Computing with AI for a Smarter Power Grid
- Oak Ridge National Laboratory News: A New Design for Quantum Computer–Monitored Electrical Grids
- Communications of the ACM: Tying Quantum Computing to AI Prompts Smarter Power Grid
- Oak Ridge National Laboratory Review: Serving up bleeding-edge compute power and expertise to the world’s scientists
- Smart Energy: Quantum computing shows potential for power system fault analysis
- Mirage News: Tying quantum computing to AI prompts smarter power grid
- ElectronicsForu: Combining Quantum Computing And AI For Smart Power Grids
- Qianzhan, Baidu, and 163
- The Quantum Insider: Quantum AI Framework Targets Energy Intensive Data Centers
Highlighted Publications
Our contributions to quantum AI for science are exemplified by our recent publications in prestigious journals:
- Conchello, R., Ajagekar, A., Bergman, M.T., Hall, C.K.*, & You, F.* (2024). Designing Microplastic-binding Peptides with a Variational Quantum Circuit-based Hybrid Quantum-Classical Approach. Science Advances, 10, eadq8492.
- Dhoriyani, J., Bergman, M.T., Hall, C.K.*, & You, F.* (2025). Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution. PNAS Nexus, DOI: 10.1093/pnasnexus/pgae572
- Ajagekar, A., & You, F.* (2023). Molecular Design with Automated Quantum Computing-based Deep Learning and Optimization. npj Computational Materials, 9, 143.
Led by PEESE graduate student Akshay Ajagekar, we explore diverse applications of quantum computing and AI:
Research Papers
- Ajagekar, A., & You, F.* (2024). Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization. Advances in Applied Energy, 14, 100179.
- Ajagekar, A., & You, F.* (2024). Variational Quantum Circuit Based Demand Response in Buildings Leveraging a Hybrid Quantum-Classical Strategy. Applied Energy, 364, 123244.
- Ajagekar, A., Al Hamoud, K., & You, F.* (2022). Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling. IEEE Transactions on Quantum Engineering, 3, 3102216.
- Ajagekar, A., & You, F.* (2021). Quantum Computing based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems. Applied Energy, 303, 117628.
- Ajagekar, A., & You, F.* (2020). Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems. Computers & Chemical Engineering, 143, 107119.
- Ajagekar, A., Humble, T., & You, F.* (2020). Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems. Computers & Chemical Engineering, 132, 106630.
Review and Perspective Articles
- Ajagekar, A., & You, F.* (2022). Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality. Renewable & Sustainable Energy Reviews, 165, 112493.
- Bernal, D.E., Ajagekar, A., Harwood, S.M., Stober, S.T., Trenev, D., & You, F.* (2022). Perspectives of Quantum Computing for Chemical Engineering, AIChE Journal, 68, e17651. [Cover Art of June 2022 issue of AIChE Journal]
- A lay summary of this Perspective article was featured in Chemical Engineering Progress (CEP).
- Ajagekar, A., & You, F.* (2022). New Frontiers of Quantum Computing in Chemical Engineering. Korean Journal of Chemical Engineering, 39, 811–820.
- Ajagekar, A., & You, F.* (2019). Quantum computing for energy systems optimization: Challenges and opportunities. Energy, 179, 76-89.
Patents and Practical Applications
Our innovative solutions have resulted in multiple patents for hybrid quantum-classical computing strategies, including:
- Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems. Patent US11769070B2 (Anticipated expiration: Oct. 9, 2040).
- Quantum computing based deep learning for detection, diagnosis and other applications. Patent Application US17/796,154.
- Simulation-based optimization framework for controlling electric vehicles. Patent Application US18/019,657.
- Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems. Patent Application EP4042298A1.
- Quantum Computing-Enhanced Systems and Methods for Large-Scale Constrained Optimization. Patent Application US18/367,155