(564b) Investigating the Dynamic Response of a Process to Control Implemented on a Quantum Computer

Traditional process control systems are classical computers which receive feedback from a chemical manufacturing process and utilize a control law to compute the control input that is implemented on the process to operate it in a safe and economical manner. As a result, advances in computing systems are important to analyze within a control context, to see whether they may hold benefits. One advance in computing that is receiving some attention within the process systems engineering community is quantum computation [1-5]. Quantum computing is a computing methodology based on quantum principles, and as a result quantum devices operate on fundamentally different principles than classical computers (and thus can run fundamentally different algorithms).

A key challenge for quantum computation today is that the internal states of a quantum computer are susceptible to noise within quantum circuit, and therefore may introduce nondeterminism (errors) in the computations performed by a quantum computer [6]. An additional source of nondeterminism may be the quantum algorithm itself. As a result of both of these sources of non-determinism, it is unclear how applicable quantum algorithms may be within a process control context. The accuracy of the control input computed by a process control system is a crucial requirement to ensure the stability and profitable operation of a chemical manufacturing process. Consequently, handling nondeterminism within quantum circuits is an important consideration for the successful implementation of process control laws on a quantum computer. Motivated by this, prior work by our group considered the analysis and design of control algorithms that may be implemented on a quantum computer [3-5]. In [4], an analysis was presented which considered the application of a quantum algorithm (based on Grover’s algorithm [6]) that searches for optimal paths over a graph as a means to implement an MPC on an illustrative chemical process example. The proposed work considered control implemented on a noise-free quantum computer. In [5], an analysis considering if the nondeterminism within a quantum circuit may fortify a process control system against cyberattacks was presented. The analysis revealed that nondeterminism within a quantum circuit does not, on its own, protect a system from cyberattacks on the process control system. Finally, in [3], the stability of an illustrative process example in the presence of different levels of errors simulating the noise within quantum circuits was analyzed. Our prior analyses have focused primarily on pathfinding examples (e.g., a process example with a single state and a single control input with a linear control law computed using a deterministic quantum algorithm). However, chemical manufacturing processes have complex process dynamics and the dynamic response of the illustrative process example to nondeterminism may not be indicative of the dynamic response of a real-time chemical process.

In this work, we seek to better understand the impacts of noise and non-determinism on control implemented with quantum devices on chemical processes. In particular, we consider several key practical issues: 1) the significance of the noise (i.e., tolerable levels of noise for meeting production targets, and how the noise interacts with other process disturbances); 2) the role of time constants in, for example, sensors in damping out the noise; and 3) the effects of spatial-temporal phenomena in damping out the noise. To analyze these points, we will consider proportional-integral-derivative (PID)-type control laws implemented with at least one of the additions in the computations implemented with a quantum simulator with noise [8]. We will consider this control law for a benchmark continuous stirred tank reactor (CSTR) with sensor noise and process disturbances, and analyze how the added noise from the quantum device impacts the closed-loop performance. Next, we will consider a wafer temperature control (WTC) system supporting the plasma etching process for the semiconductor industry [7]. A model that we developed for this process in [7] contains the information about the dynamics of the actuators and the sensors, and therefore can be used for analyzing the role of the quantum noise (and the sampling period/input selection frequency) on sensor readings. In particular, we analyze the conditions under which the fluctuations due to the quantum noise are smoothed out, and when the quantum noise effects become more prominent. Finally, we will discuss how distributed parameter systems, simulated in ANSYS Fluent, respond to the noise in the device. For this, we will utilize a benchmark simulation of flow in a pipe with a sensor simulated within the pipe [9]. We will develop a workflow for simulating quantum algorithms with noise though user-defined functions (UDF's) in Fluent, and will then analyze the extent to which both the spatio-temporal dynamics, as well as sensor dynamics, cause the impacts of the noise from the controller to be damped out or amplified.

References:

[1] A., Ajagekar, and F., You, “New frontiers of quantum computing in chemical engineering”, Korean Journal of Chemical Engineering, volume 39, pp. 811–820, 2022.

[2] Nourbakhsh A, Jones M.N., Kristjuhan K., Carberry D., Karon J., Beenfeldt C., Shahriari K., Andersson M. P., Jadidi M. A., Mansouri S.S., “Quantum computing: Fundamentals, trends and perspectives for chemical and biochemical engineers”, arXiv preprint arXiv:2201.02823, 2022.

[3] Nieman, K., Rangan, K. K., and Durand, H., “Control Implemented on Quantum Computers: Effects of Noise, Non-Determinism, and Entanglement”, Industrial & Engineering Chemistry Research, volume 61(28), pp. 10133-10155, 2022.

[4] Nieman, K., Durand, H., Patel, S., Koch, D. and Alsing, P.M., “Investigating an amplitude amplification-based optimization algorithm for model predictive control”, Digital Chemical Engineering, volume 10, p.100134, 2024.

[5] Rangan, K. 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”, Proceedings of the 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, volume 55(7), pp. 703-708, Busan, Republic of Korea, 14–17 June 2022.

[6] Córcoles, A.D., Kandala, A., Javadi-Abhari, A., McClure, D.T., Cross, A.W., Temme, K., Nation, P.D., Steffen, M., Gambetta, J.M., “Challenges and opportunities of near-term quantum computing systems” Proceedings of the IEEE, volume 108(8), pp. 1338-1352, December 2019.

[7] Oyama, H., Nieman, K., Tran, A., Keville, B., Wu, Y., Durand, H., “Computational fluid dynamics modeling of a wafer etch temperature control system”, Digital Chemical Engineering, volume 8, p. 100102, 2023.

[8] Norlén H., “Quantum Computing in Practice with Qiskit® and IBM Quantum Experience®: Practical recipes for quantum computer coding at the gate and algorithm level with Python”, Packt Publishing Ltd, 2020.

[9] Rangan, Keshav Kasturi, Henrique Oyama, Ilham Azali Assoumani, Helen Durand, and K. Y. Simon Ng. "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.