Distributed Amplitude Amplification for Evaluating Noisy Intermediate-Scale Quantum Devices in Engineering-Relevant Computations

In many fields of engineering, controllers are used to enable automatic adjustment to actuators to keep dynamic systems operating according to pre-specified operating goals. For example, in chemicals manufacturing, controllers might be used to keep process variables such as temperature, pressure, and concentration on target to ensure both safety of process operation and quality of the chemicals produced. Prior work in our group has looked at topics such as how amplitude amplification might be used to implement an advanced control law (e.g., [1,2,3]). However, we would expect meaningful control problems, and also other meaningful engineering-relevant computations such as anomaly detection, to require a larger gate depth than could be executed as intended on a noisy intermediate-scale quantum (NISQ) device. Motivated by the need to understand when we might be able to implement engineering-relevant computations on NISQ devices, this work will explore the concept of distributing the computations, inspired by results in distributed optimization and control [4,5].



[1] Nieman, K., Durand, H., Patel, S., Koch, D., & Alsing, P. M. (2024). Investigating amplitude amplification in optimization-based control for a continuous stirred tank reactor. Digital Chemical Engineering, 100180.

[2] Nieman, K., Durand, H., Patel, S., Koch, D., & Alsing, P. M. (2024). Parallelizing process model integration for model predictive control through oracle design and analysis for a Grover’s algorithm-inspired optimization strategy. Digital Chemical Engineering, 13, 100179.

[3] Nieman, K., Durand, H., Patel, S., Koch, D., & Alsing, P. M. (2024). Investigating an amplitude amplification-based optimization algorithm for model predictive control. Digital Chemical Engineering, 10, 100134.

[4] Yang, T., Yi, X., Wu, J., Yuan, Y., Wu, D., Meng, Z., Hong, Y., Wang, H., Lin, Z., & Johansson, K. H. (2019). A survey of distributed optimization. Annual Reviews in Control, 47, 278-305.

[5] Christofides, P. D., Scattolini, R., De La Pena, D. M., & Liu, J. (2013). Distributed model predictive control: A tutorial review and future research directions. Computers & Chemical Engineering, 51, 21-41.