(149j) Practical Control Laws with Quantum Computation

Quantum computing has been receiving interest in chemical engineering (e.g., [2] [3]). Chemical engineering involves a broad range of science and engineering problems related for example to transport phenomena, optimization, design and modeling of complex systems [6]. Applications of quantum devices for chemical engineering applications have included using a hybrid quantum/classical approach to solve process scheduling problems in [7]. Quantum computing combined with quantum artificial intelligence was also elaborated for renewable and sustainable energy systems in [8]. In addition, quantum computing has been receiving engineering attention for applications such as control (e.g., [1]). In [1] and [9], proportional control algorithms implemented on quantum computers were investigated. However, proportional-integral control is often more preferable than proportional-only control. Therefore, to move toward practical control algorithm implementations on quantum computers, it is necessary to consider a proportional-integral control formulation, and also how controllers might be more systematically designed.

In this talk, we discuss implementing proportional-integral control with the aid of a quantum computer, using an addition code from [10] that takes advantage of the Quantum Fourier Transform for implementing the control law. We also build on the discussion from [4] in which, inspired by learning of a quantum circuit in [11], we discussed how an integer program might be developed for attempting to learn an algorithm for quantum chemistry computations for a single qubit, and discussed how the single qubit would not be able to take advantage of all of the properties of quantum computers. We expand the discussion here to present how integer programs might in general be developed for gate selection for specific state measurement/control output relationships to find the gates which can represent a specific action desired by a control law. However, we also will discuss how this does not constitute learning an "algorithm" like the Quantum Fourier Transform-based addition algorithm [12], because such algorithms are able to adapt a relationship that can be coded to different numbers of total qubits and should correspond to different state/input relationships without the need to "re-code" them. We will discuss the implications of control algorithms on quantum computers for control of both traditional process-scale systems as well as smaller-scale systems. Specifically, quantum computing was employed to design an optimally-shaped electromagnetic field in order to control molecular systems on a quantum basis in [5]. In the last part of our talk, we will introduce principles of quantum control in the context of the control of the bond length of an HF molecule and discuss the challenges of implementing the control algorithms developed for the classical devices (such as quantum computing-implemented proportional and proportional-integral control) for the quantum system, which result due to the measurement principles of quantum mechanics that affect feedback control.

References:

[1] Nieman, K., Kasturi Rangan, K., and Durand, H., “Control implemented on quantum computers: Effects of noise, nondeterminism, and entanglement,” Ind. Eng. Chem. Res. 61(28), 10133–10155 (2022).

[2] Akshay Ajagekar and Fengqi You. “Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems”. In: Applied Energy 303 (2021), p. 117628.

[3] Akshay Ajagekar and Fengqi You. “Quantum computing for energy systems optimization: Challenges and opportunities”. In: Energy 179 (2019), pp. 76–89

[4] 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

[5] Alicia B Magann et al. “Digital quantum simulation of molecular dynamics and control”. In: Physical Review Research 3.2 (2021), p. 023165.

[6] Ajagekar, A. and You, F., 2022. New frontiers of quantum computing in chemical engineering. Korean Journal of Chemical Engineering, 39(4), pp.811-820.

[7] Tran, T., Do, M., Rieffel, E., Frank, J., Wang, Z., O'Gorman, B., Venturelli, D. and Beck, J., 2016. A hybrid quantum-classical approach to solving scheduling problems. In Proceedings of the International Symposium on Combinatorial Search (Vol. 7, No. 1, pp. 98-106).

[8] Ajagekar, A. and You, F., “Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality,” Renew. Sustain. Energy Rev. 165, 112493 (2022).

[9] 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.

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

[11] Cincio, L., Subaşı, Y., Sornborger, A. T., & Coles, P. J. (2018). Learning the quantum algorithm for state overlap. New Journal of Physics, 20(11), 113022.

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