(268f) Control Strategies Using Quantum Computations for Next-Generation Manufacturing

Since Industry 4.0 standards were introduced, the importance of collecting and processing data has skyrocketed to ensure that industries not only improve their process efficiency, but also transparency. Chemical processing demands are increasing along with those in multiple other fields. Quantum computation, as a new computing technique, is, as a result, receiving interest in a variety of fields [1], including process systems engineering [2]. From a controls perspective, however, the utility of quantum computing remains unclear. However, we hypothesize that by implementing controllers on quantum computers (initially starting with simpler control laws and then progressing to more advanced control laws), we will be able to gain insights into how and whether quantum computation could be useful for process control.

With this motivation, the concept of quantum computing from a controls perspective is investigated by controlling a simple linear system using a proportional (P) controller implemented in a hybrid quantum-classical fashion [3], where classical pre-processing is used to prepare data for summation to create the result of the multiplication. This is achieved by using a Quantum Fourier Transform-based [4] addition algorithm on IBM’s quantum experience SDK, Qiskit. This leverages the code in [7] for addition with the quantum computer. The algorithm will then be compared to an implementation of the same control strategy as a quantum simulation that does not use the same classical pre-processing to analyze the impact of quantum noise on different algorithm structures. A variety of factors that influence the results will be analyzed, including the process states measured, number of shots and quantum register size used to represent process states [3,5,6]. We will discuss noise models and how the noise models affect the theory and practice of quantum computing-implemented control.

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] Keshav Kasturi Rangan et al. “Quantum Computing and Resilient Design Perspectives for Cybersecurity of Feedback Systems”. In: IFAC-PapersOnLine 55.7 (2022), pp. 703–708

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

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

[6] Kasturi Rangan, K., Oyama, H., Azali Assoumani, I., Durand, H., & Ng, K. Y. S. (2023). 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.

[7]Anagolum, S. DoNew. 2018. https://github.com/SashwatAnagolum/DoNew.