Document Type
Article
Abstract
A major challenge to determining the applicability (and potential outperformance over classical computers) of a quantum computer (QC) within chemical manufacturing processes is quantum noise. Computations by a QC are error-prone due to the influence of quantum noise inherent to the hardware. Errors in control inputs may destabilize a chemical process and lead to unsafe conditions for manufacturing personnel and the environment. The response of a process with control implemented on a QC to errors due to noise must be investigated thoroughly. In this work, the impacts of control input errors due to quantum noise on a process are modeled as bounded additive exogenous signals. Process and control element dynamics dictate the response of the process due to perturbations from exogenous signals. A theoretical analysis of the influence of process and control element dynamics on the response of a process to errors in control implemented on a QC is presented. Using simulations of a process example, a demonstration of the interplay between process response to noise, process dynamics, and control valve dynamics is illustrated. The results evaluate, for a specific process, aspects of how process and control element dynamics may mitigate the impact of noise.
Disciplines
Controls and Control Theory | Information Security | Process Control and Systems
Recommended Citation
S. Narasimhan, D. Messina, H. Oyama and H. Durand, "Response of Dynamic Processes with Control Implemented on a Noisy Quantum Computer," 2025 American Control Conference (ACC), Denver, CO, USA, 2025, pp. 4879-4884, doi: 10.23919/ACC63710.2025.11108027.
Included in
Controls and Control Theory Commons, Information Security Commons, Process Control and Systems Commons
Comments
© American Automatic Control Council (AACC) 2025. Peer Reviewed Conference Proceeding, 2025 American Control Conference (ACC), July 8-10, 2025, Denver, CO, USA. Originally published at https://doi.org/10.23919/ACC63710.2025.11108027. Financial support from the National Science Foundation CBET- 2143469, CBET-1839675, CNS-1932026, Lam Research Corporation, and Wayne State University is gratefully acknowledged.