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.
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.
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.
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.
Quantum computing is emerging as a transformative paradigm for molecular-level modeling in process engineering, offering the potential to perform large-scale, high-accuracy simulations that surpass the computational limits of classical methods. However, the inherent noise and decoherence in quantum systems present major obstacles to reliable molecular interaction predictions. This research conducts a qualitative investigation of error-mitigation strategies designed to improve the accuracy and stability of quantum simulations for modeling molecular interactions relevant to chemical and process engineering. By reviewing scientific literature and analyzing case studies across quantum mechanics, computational chemistry, and industrial process modeling, the study identifies the principal challenges associated with quantum noise, gate fidelity, and algorithmic scalability. A conceptual framework was developed to evaluate how quantum error correction (QEC) techniques, zero-noise extrapolation, and hybrid quantum–classical algorithms enhance predictive precision in molecular simulations. The findings suggest that combining these methods not only mitigates computational noise but also improves convergence efficiency in modeling reaction pathways and intermolecular potentials. This research synthesizes interdisciplinary insights to propose a roadmap for integrating quantum-enhanced molecular simulations into sustainable process engineering. It underscores how advances in error mitigation will be essential for achieving trustworthy, energy-efficient, and scalable industrial applications of quantum computation in the coming decade.
Quantum computing algorithms are usually built by using gates and circuits to manipulate qubits. In this article, our analysis examines the impact of additive noise in a quantum circuit as an engineering simplification model. We evaluate these effects by computing the probability, fidelity, and signal-to-noise ratio (SNR). We study quantum noise from an amplitude-domain signal-processing perspective, bridging classical additive noise models with quantum state perturbations. We have analyzed the results of various quantum gates, such as X, Y, Z, and Hadamard gates. This study provides an approximation of the quantitative effect and qualitative analysis of the effect of additive noise on quantum gates, thereby contributing to a deeper understanding of the challenges and potential solutions in quantum computing. However, in some cases, the model does not represent the physical results due to approximation.
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.