Sponsored by the National Science Foundation (NSF)
This project addresses critical challenges in cyber-physical systems optimization by developing innovative quantum-classical algorithms to solve complex mixed-binary optimization problems. By leveraging quantum computing and machine learning, the project aims to pioneer decomposition and distributed approaches for tackling deterministic and stochastic problems in the power grid domain.
The proposed methods will transform large-scale optimization challenges into manageable subproblems, enabling efficient collaboration between quantum and classical computing systems. Key innovations include the development of trainable variational quantum algorithms integrated with intelligent quantum Lagrangian and Benders techniques. These advancements will ensure seamless coordination between quantum and classical machines, unlocking new possibilities for optimization in cyber-physical systems.
This project explores using Long Short-Term Memory (LSTM) models to optimize parameters in the Quantum Approximate Optimization Algorithm (QAOA). Instead of direct optimization, the LSTM predicts parameters based on input problem instances, offering a scalable alternative for complex optimization tasks.
Amin Kargarian (LSU ECE Department)
Ramachandran Vaidyanathan (LSU ECE Department)
Omar Magana-Loaiza (LSU Department of Physics & Astronomy)
Reza Mahroo (PhD)
Farshad Amani (PhD)
Milad Hasanzadeh (PhD)
Riley Dawkins (PhD)
F. Amani, A. Kargarian, "Optimal Power Flow Solution via Noise-Resilient Quantum Interior-Point Methods," Electric Power Systems Research, 2024.
F. Amani, A. Kargarian, "Quantum-inspired Optimal Power Flow," IEEE Texas Power and Energy Conference, 2024.
R. Dawkins, M. Hong, C. You, O. Magaña-Loaiza, Omar_S "The quantum GaussianSchell model: a link between classical and quantum optics" Optics Letters, vol. 49, 2024.
R. Mahroo, A. Kargarian, "Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling," IEEE Transactions on Quantum Engineering, vol 4, 2023.
F. Amani, R. Mahroo, A. Kargarian, "Quantum-Enhanced DC Optimal Power Flow," IEEE Texas Power and Energy Conference, 2023.
R. Mahroo, A. Kargarian, M. Mehrtash, A. Conejo, "Robust Dynamic TEP with an N-c Security Criterion: A Computationally Efficient Model," IEEE Transactions on Power Systems, vol. 38, no. 1, pp. 912 - 920, 2022.
R. Mahroo, A. Kargarian, “Hybrid Quantum-Classical Unit Commitment,” IEEE Texas Power and Energy Conference, 2022.