(350c) Intersecting Quantum Computing and Control with Materials

Recent years have seen significant advances in the manner in which materials are handled in process systems engineering, with efforts integrating process systems tools with materials discovery. For example, computer-aided molecular design methods [1], simultaneous product and process design [2], optimization-based materials design [3], and data science approaches [4] have been techniques used for moving toward more systematic computational materials creation. Despite the greater emphasis on materials design, there has not been much discussion in the process systems engineering community of materials control. However, modulation of various phenomena can induce changes in materials. For example, [5] explores using a laser to impact material optical properties. Works such as this indicate that a deeper exploration of materials control in the process systems engineering field may prove valuable within the context of the current efforts in materials engineering.

Motivated by these considerations, this work focuses on two topics related to the design and control of materials. In the first part of the talk, we discuss how the complexities of attempting to optimally design materials motivate an exploration of whether quantum computing might be useful. Specifically, inspired by [7], which uses machine learning to attempt to locate quantum circuits which give a desired result, we discuss the challenges of trying to figure out the structure of a hybrid quantum/classical algorithm that should be used for searching for a means to represent the relationship between some material properties and the ground state energy if this is not based on a pre-conceived notion based on physics [6]. For example, if it is desired to use a small number of qubits due to the fact that only one- and five-qubit devices are currently available for free from IBM’s Quantum Experience, one could ask what might be achieved on a single-qubit device. In this case, measurement of the qubits at the end of the algorithm will give a 0 or a 1, with some probability. The series of gates can modulate the probability to some extent, but only a finite set of gates is available. Using a hybrid quantum/classical algorithm, where the probabilities are then related to the ground state energy through some function, provides more tuning knobs, but also greater challenges in attempting to locate a function to use in relating the probabilities to the ground state energy when there is not a motivating physical relationship. Furthermore, some means for relating the chemistry to the algorithm steps must also be incorporated. We also discuss how noise in the quantum devices can impact the difficulty of attempting to design useful strategies via this ad hoc approach. The second part of the talk will provide a discussion of control of materials from a process systems engineering viewpoint, particularly in the context of simulation of closed-loop control of materials, discussion of materials dynamics, and discussion of the types of inputs that might be used in modulating a material property.

[1] Samudra, A. P., & Sahinidis, N. V. (2013). Optimization‐based framework for computer‐aided molecular design. AIChE Journal, 59(10), 3686-3701.

[2] Eden, M. R., Jørgensen, S. B., Gani, R., & El-Halwagi, M. M. (2004). A novel framework for simultaneous separation process and product design. Chemical Engineering and Processing: Process Intensification, 43(5), 595-608.

[3] Hanselman, C. L., & Gounaris, C. E. (2016). A mathematical optimization framework for the design of nanopatterned surfaces. AIChE Journal, 62(9), 3250-3263.

[4] Eugene, E. A., Phillip, W. A., & Dowling, A. W. (2019). Data science-enabled molecular-to-systems engineering for sustainable water treatment. Current Opinion in Chemical Engineering, 26, 122-130.

[5] Khalsa, G., Benedek, N.A. and Moses, J., 2021. Ultrafast control of material optical properties via the infrared resonant Raman effect. Physical Review X, 11(2), p.021067.

[6] Rangan, K. K., J. Abou Halloun, H. Oyama, I. Azali Assoumani, N. Jairazbhoy, H. Durand, and S. K. Ng, “Design and Control Resilience/Robustness: Relationships to Quantum Computing and Cybersecurity,” Proceedings of the IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS), Busan, Republic of Korea, in press.

[7] 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.