(250b) Impacts of Quantum Algorithms and Noise on Stability for Next-Generation Control Applications

The objective of process control is to guarantee the safety of a process while minimizing economic loss. One fundamental challenge with achieving this goal is non-determinism from various sources that can result in unexpected process behavior. A traditional goal in industrial process control is the rejection of these non-deterministic sources, such as disturbances, measurement inaccuracies/noise and changes in process dynamics as a plant operates. While many attempts have been made to address this challenge, new sources of non-determinism are introduced as a consequence of manufacturing’s quest to improve process efficiency [1]. Each new manufacturing setup could be associated with its own set of challenges and requires a thorough characterization of safety risks and mitigation techniques. One such source of non-determinism, that is likely to be encountered with the growing interest in various fields [2] is the use of quantum devices and algorithms. For example, advances in quantum computation prompt whether this emerging computing paradigm (currently affected by hardware-related quantum “noise” [3]) could ever be evaluated for control applications, or if the hardware limitation that introduces non-determinism as noise will render it unsafe for control applications.

Motivated by the need for a rigorous characterization of safety in manufacturing systems with new sources of non-determinism derived from emerging technologies, this talk will address the development of quantum arithmetic algorithms from the perspective of process control systems and the impact of quantum noise on these algorithms [4,5]. Several Quantum Fourier Transform (QFT) based multiplication algorithms [6] based on both hybrid and purely quantum formulations are utilized in developing stabilizing single input/single output control loops. Once developed, the control algorithms are analyzed in the presence of depolarizing error (a type of quantum noise). The depolarizing error will be approximated from a real quantum device available on IBM’s quantum SDK, Qiskit. The noise will be integrated into a quantum circuit run on a Qiskit quantum simulator to determine the control input needed to stabilize a process with linear dynamics. The analysis compares hybrid quantum algorithms with purely quantum algorithms in the presence of quantum noise to determine their impact on obtaining deterministic stabilizing control actions. In conclusion, we will discuss the impact of using known techniques to approximate the noise model and analyze the consequent impact on process stability.

References

[1] X. Gao, F. Yang, C. Shang, and D. Huang, “A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era,” Chinese Journal of Chemical Engineering, vol. 24, no. 8, pp.
952–962, 2016.
[2] S. Harwood, C. Gambella, D. Trenev, A. Simonetto, D. Bernal, and D. Greenberg, “Formulating and solving routing problems on quantum computers,” IEEE Transactions on Quantum Engineering, vol. 2, pp.
1–17, 2021.
[3] S. Chen, J. Cotler, H.-Y. Huang, and J. Li, “The complexity of nisq,” Nature Communications, vol. 14, no. 1, p. 6001, 2023.
[4] K. Nieman, K. Kasturi Rangan, and H. Durand, “Control implemented on quantum computers: Effects of noise, nondeterminism, and entanglement,” Industrial & Engineering Chemistry Research, vol. 61, no. 28, pp. 10 133–10 155, 2022.
[5] K. K. Rangan, J. Abou Halloun, H. Oyama, S. Cherney, I. A. Assoumani, N. Jairazbhoy, H. Durand, and S. K. Ng, “Quantum computing and resilient design perspectives for cybersecurity of feedback systems,” IFAC-PapersOnLine, vol. 55, no. 7, pp. 703–708, 2022.
[6] K. K. Rangan, H. Oyama, I. A. Assoumani, H. Durand, and K. 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. AIP Publishing LLC Melville, New York, 2023.