Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on a quantum simulator with noise, evaluation of potential impacts of nondeterminism on theory for advanced control laws, and discussion of consequences of the way that entanglement works for next-generation manufacturing communication objectives.
Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on a quantum simulator with noise, evaluation of potential impacts of nondeterminism on theory for advanced control laws, and discussion of consequences of the way that entanglement works for next-generation manufacturing communication objectives.
A major challenge to determining the applicability (and potential outperformance over classical computers) of a quantum computer (QC) within chemical manufacturing processes is quantum noise. Computations by a QC are error-prone due to the influence of quantum noise inherent to the hardware. Errors in control inputs may destabilize a chemical process and lead to unsafe conditions for manufacturing personnel and the environment. The response of a process with control implemented on a QC to errors due to noise must be investigated thoroughly. In this work, the impacts of control input errors due to quantum noise on a process are modeled as bounded additive exogenous signals. Process and control element dynamics dictate the response of the process due to perturbations from exogenous signals. A theoretical analysis of the influence of process and control element dynamics on the response of a process to errors in control implemented on a QC is presented. Using simulations of a process example, a demonstration of the interplay between process response to noise, process dynamics, and control valve dynamics is illustrated. The results evaluate, for a specific process, aspects of how process and control element dynamics may mitigate the impact of noise.
A major challenge to determining the applicability (and potential outperformance over classical computers) of a quantum computer (QC) within chemical manufacturing processes is quantum noise. Computations by a QC are error-prone due to the influence of quantum noise inherent to the hardware. Errors in control inputs may destabilize a chemical process and lead to unsafe conditions for manufacturing personnel and the environment. The response of a process with control implemented on a QC to errors due to noise must be investigated thoroughly. In this work, the impacts of control input errors due to quantum noise on a process are modeled as bounded additive exogenous signals. Process and control element dynamics dictate the response of the process due to perturbations from exogenous signals. A theoretical analysis of the influence of process and control element dynamics on the response of a process to errors in control implemented on a QC is presented. Using simulations of a process example, a demonstration of the interplay between process response to noise, process dynamics, and control valve dynamics is illustrated. The results evaluate, for a specific process, aspects of how process and control element dynamics may mitigate the impact of noise.
Processing and storage demands of industrial processes are causing fields such as optimization, scheduling, and control to assess the effectiveness of quantum devices in their applications. A key objective of control systems is to ensure process safety. This paper focuses on the potential of quantum devices to compute control inputs that maintain system safety despite sources of nondeterminism inherent to currently available quantum devices (quantum noise). In our previous work, we employed a quantum simulator to assess whether a quantum implementation of a proportional (P) control law could stabilize a single-input/single-output system under quantum noise approximated from a real quantum device. While one algorithm achieved this objective, another performed poorly, raising questions about the underlying reasons for their differing performance. This study explores how conventional control engineering techniques for handling nondeterminism (e.g., modifying sampling period lengths or applying only stabilizing control actions based on steady-state tracking) could enhance system response in the presence of noise. Utilizing IBM’s Qiskit, a quantum simulator was used to evaluate algorithmic and heuristic control strategies to improve the steady-state tracking performance of a P-control law under quantum noise. The objective here is to provide insight into the feasibility of near-term quantum devices to execute control actions with safety considerations.
Quantum computing comes with the potential to push computational boundaries in various domains including, e.g., cryptography, simulation, optimization, and machine learning. Exploiting the principles of quantum mechanics, new algorithms can be developed with capabilities that are unprecedented by classical computers. However, the experimental realization of quantum devices is an active field of research with enormous open challenges, including robustness against noise and scalabilty. While systems and control theory plays a crucial role in tackling these challenges, the principles of quantum physics lead to a (perceived) high entry barrier for entering the field of quantum computing. This tutorial paper aims at lowering the barrier by introducing basic concepts required to understand and solve research problems in quantum systems. First, we introduce fundamentals of quantum algorithms, ranging from basic ingredients such as qubits and quantum logic gates to prominent examples and more advanced concepts, e.g., variational quantum algorithms. Next, we formalize some engineering questions for building quantum devices in the real world, which requires the careful manipulation of microscopic quantities obeying quantum effects. To this end for N-level systems, we introduce basic concepts of (bilinear) quantum systems and control theory including controllability, observability, and optimal control in a unified frame. Finally, we address the problem of noise in real-world quantum systems via robust quantum control, which relies on a set-membership uncertainty description frequently employed in control. A key goal of this tutorial paper is to demystify engineering aspects of quantum computing by emphasizing that its mathematical description mainly involves linear algebra (for quantum algorithms) and the handling of bilinear control systems (for quantum systems and control theory) but does not require too much detailed knowledge of quantum physics.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.
Classical computing systems find utility in performing engineering computations in both design and operation of chemical manufacturing processes. During operation, computing systems may be used within process control systems (PCSs) to operate processes by computing control actions. Control engineers may eventually need to determine the utility of other computing devices such as quantum devices (QCs) within PCSs. A QC that is not fault-tolerant may introduce errors in control computations due to the noise (e.g., quantum noise) and result in the process states (e.g., temperature, concentration) deviating from desired operating limits. Deviations of process states may result in one or both of two undesirable consequences that need to be addressed: (1) unsafe situations (e.g., due to loss of containment) and (2) reduction in the profitability of the process (e.g., due to low quality product). Thus, it is currently unclear to what extent errors in noisy intermediate-scale quantum (NISQ) devices would need to be eliminated before they could have potential utility for control action computation. In the current work, we investigate how control theory might aid in guiding answers to this question. We first characterize the stability of linear processes with proportional control implemented on a QC by treating quantum noise as a bounded disturbance exogenous to the process. Then, we analyze the extent to which an optimal control formulation for processes with noise and plant/model mismatch might apply to processes with unexpected control actions from a NISQ device. We demonstrate the results using a single-input/single-output system under a control law implemented using a quantum simulator with a depolarizing error noise model affecting the control action computations.