PEESE research on quantum computing and AI for smart energy systems was reported by some news media. Some recent ones include, but are not limited to, the ones below:
- Cornell Chronicle: Tying quantum computing to AI prompts a smarter power grid
- Tech Xplore and 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
- RoxxCloud: Linking quantum computing to AI leads to a smarter electricity grid
- ElectronicsForu: Combining Quantum Computing And AI For Smart Power Grids
- Qianzhan, Baidu, and 163
These are for our recent paper titled “Quantum Computing-based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems.”, as well as an earlier paper on quantum computing-based deep learning for fault detection and diagnosis in industrial manufacturing. A patent was filed for this invention.
Relevant papers from PEESE grad student Akshay Ajagekar are listed below:
- 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., & You, F.* (2023). Molecular Design with Automated Quantum Computing-based Deep Learning and Optimization. npj Computational Materials, 9, 143.
- 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.
- 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]
- 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.