Intersection of Control with Quantum Computing

Professor Helen Durand is an Associate Professor at Wayne State University and studies the intersection of Chemical Engineering, Materials Science, and Electrical and Computer Engineering. Her research focuses on developing advanced control systems for next-generation manufacturing and cyberphysical systems. With a strong emphasis on "smart" manufacturing, she addresses the integration of computing, sensing, and networking to enhance production efficiency, agility, and autonomy. Her work contributes to innovations in cyberattack detection for nonlinear systems, quantum computing applications in control, digital twin design for dynamic processes, and virtual testing of image-based control designs. Helen earned her Ph.D., M.S., and B.S. from the University of California, Los Angeles.
Quantum computing offers transformative potential for several areas specific to chemical engineering, especially in problems where traditional computational methods struggle with complexity and accuracy. Here are some key applications:
Molecular Simulation: Design catalysts, model reaction mechanisms, and accelerate drug discovery with quantum precision.
Material Design: Develop polymers, energy storage materials, and efficient separation membranes using quantum simulations.
Thermodynamic Calculations: Accurately predict phase behavior for complex fluid systems in separation processes.
Structure Prediction: Enhance crystal structure and protein folding predictions for materials and biochemical engineering.
Environmental Applications: Improve carbon capture technologies and optimize pollution control and waste management.
Reaction Network Analysis: Solve complex reaction networks in combustion and biochemical processes.
Data Analysis: Use quantum machine learning for process optimization and anomaly detection in large datasets.
Kinetics and Reaction Engineering: Simulate enzyme interactions and predict reaction rates for reactor design.
Specifically for process dynamics and control, there are additional applications:
Model Predictive Control (MPC): Optimize MPC for large-scale and nonlinear systems with quantum algorithms, enabling faster real-time decision-making.
Process Optimization: Solve complex, multi-variable optimization problems in process control more efficiently, improving overall system performance.
Adaptive and Robust Control: Develop quantum-based strategies for adaptive and robust control, especially in systems with high uncertainty or variability.
Fault Detection and Diagnosis: Enhance early detection of process anomalies using quantum-enhanced data analysis for better system reliability.
State Estimation: Use quantum algorithms to improve the speed and accuracy of state estimation methods, such as the Kalman filter, for better process monitoring.
Complex Control System Simulation: Simulate and analyze highly complex control systems to test and validate control strategies before implementation.
Optimization of Control Parameters: Rapidly optimize tuning parameters for controllers, like PID controllers, across a wide range of operating conditions.
Networked Control Systems: Manage and optimize distributed control systems more efficiently using quantum approaches to handle communication delays and coordination.

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