2025 AIChE Annual Meeting

(594g) Hybrid Quantum Algorithm Strategies to Stabilize Process Systems Susceptible to Quantum Noise

Authors

Helen Durand, Wayne State University
Modern industries, influenced by cyber-physical systems (CPS), have increased the integration of physical processes with various computer, control and networking components. This has increased the importance of data processing to achieve improvements in process efficiency. Currently available (classical) computer chips and algorithms are struggling to meet these increasing demands in data processing and storage, which is not unique to only chemical industries but applies to multiple other fields as well. Quantum computation is receiving interest in a variety of fields [1], including process systems engineering [2] because it may have the capacity to handle some of these higher processing requirements. From the perspective of its use in control engineering, however, quantum computing remains relatively unexplored. This motivated prior research [3,4,5] from our group to investigate quantum computing from a controls perspective by controlling a single-input/single-output linear system using a proportional (P) controller implemented in a hybrid quantum-classical fashion [3] using Quantum Fourier Transform-based [6] addition and multiplication algorithms on quantum simulators provided by IBM’s quantum experience SDK, Qiskit. The simulations were pivotal in demonstrating the relationship between the impact of quantum noise and factors, such as the magnitude of the process state, number of shots, quantum register size used to represent the process states, and sampling period length, to mitigate the impact of quantum noise on a chemical process under a control law implemented on a noisy quantum simulator [7].

While these heuristic approaches demonstrate some of these relationships between steady-state tracking and control heuristics, they do not provide a theoretical framework for investigating the impacts of quantum noise models on closed-loop stability. Quantum noise models are represented within the context of Kraus operators and density matrices and mitigating their influence in quantum devices remains an open area of research across multiple fields [8,9]. Therefore, we must rigorously characterize the closed-loop behavior of a process under a controller implemented using a noisy quantum device by developing a strategy that correlates the predictions of quantum computations (in the presence of a noise model) with their impact on closed-loop stability. By incorporating models of quantum noise affecting quantum devices within closed-loop system dynamics, we can extend heuristic approaches for improving closed-loop performance and quantitatively evaluate the probability with which control actions computed by noisy quantum devices can ensure process stability (at least some percentage of the time). The goal of this work is to develop a deeper understanding of how quantum computing can be integrated into control theory, enabling the design of next-generation control algorithms that exploit quantum properties to improve efficiency and performance.

References:

[1] Deng, Z., Wang, X., & Dong, B. (2023). Quantum computing for future real-time building HVAC controls. Applied Energy, 334, 120621.

[2] Ajagekar, A., & You, F. (2020). Quantum computing assisted deep learning for fault detection and diagnosis in industrial process systems. Computers & Chemical Engineering, 143, 107119.

[3] Kasturi Rangan, K., Abou Halloun, J., Oyama, H., Cherney, S., Assoumani, I.A., Jairazbhoy, N., Durand, H. and Ng, S.K. Quantum computing and resilient design perspectives for cybersecurity of feedback systems. IFAC-PapersOnLine, 55(7), 703-708 (2022).

[4] Nieman, K., Kasturi Rangan, K., & Durand, H. Control Implemented on Quantum Computers: Effects of Noise, Nondeterminism, and Entanglement. Industrial & Engineering Chemistry Research, 61(28), 10133-10155 (2022).

[5] Kasturi Rangan, K., Oyama, H., Azali Assoumani, I., Durand, H., & Ng, K. Y. S. Cyberphysical Systems and Energy: A Discussion with Reference to an Enhanced Geothermal Process. In Energy Systems and Processes: Recent Advances in Design and Control (pp. 8-1). Melville, New York: AIP Publishing LLC (2023).

[6] Ruiz-Perez, L., & Garcia-Escartin, J. C. Quantum arithmetic with the quantum Fourier transform. Quantum Information Processing, 16(6), 1-14 (2017).

[7] Kasturi Rangan, K., and Durand, H. Investigating Quantum Algorithm and Control Design Intersections through a Proportional Control

Law. Proceedings of the American Control Conference, Denver, Colorado, (in print).

[8] Roffe, J. Quantum error correction: an introductory guide. Contemporary Physics, 60(3), 226-245 (2019).

[9] Google Quantum AI Research. Suppressing quantum errors by scaling a surface code logical qubit. Nature 614, no. 7949: 676-681 (2023).