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.
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 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.
Recent increasing interest in quantum computers has spurred research into practical engineering applications for quantum algorithms. One potential application is process control. The unique quantum phenomena involved with quantum computing brings up interesting considerations for control. This work focuses on non-determinism, first through a motivating simulation utilizing a continuous stirred-tank reactor. Following this, two methods of potentially ensuring system stability in the presence of non-determinism are discussed. The first involves including an additional gate to the modified Grover’s algorithm presented in our previous work, which is designed to prevent a qubit state corresponding to an undesired control input from being measured. The second method involves defining an inner region where, if the state leaves the region, a classical stabilizing controller is activated to drive the state back inside the region.
The Quantum Approximate Optimization Algorithm (QAOA) is widely regarded as a promising near-term quantum algorithm for addressing hard combinatorial optimization problems on Noisy Intermediate-Scale Quantum (NISQ) devices; however, its real-world effectiveness is severely constrained by hardware noise, including gate infidelities, decoherence, and measurement errors. This work explores an error mitigation–driven approach to enhance the performance and robustness of QAOA without altering its algorithmic structure or requiring fault-tolerant quantum resources. Measurement error mitigation and zero-noise extrapolation are incorporated into the execution and post-processing stages of QAOA circuits and evaluated using the Minimum Vertex Cover problem as a representative benchmark. Performance is assessed through approximation ratios, solution stability, and noise sensitivity across multiple circuit depths. The results indicate that error mitigation not only improves approximation accuracy but also stabilizes output distributions and delays performance degradation at increased circuit depths. Additionally, the mitigated executions demonstrate better consistency across repeated runs, highlighting improved resilience to stochastic noise. These findings emphasize that lightweight and hardware-efficient error mitigation techniques can significantly extend the practical usability of QAOA on current NISQ hardware, providing a scalable and realistic pathway for executing quantum optimization algorithms more reliably in near-term quantum computing environments.
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.
This paper investigates the potential of adaptive control systems utilizing quantum feedback loops for achieving enhanced control performance in complex dynamic systems. Classical control methods often struggle with the inherent uncertainties and nonlinearities present in many real-world systems. We propose a novel approach that leverages the principles of quantum mechanics, specifically quantum measurement and feedback, to directly influence system dynamics. The core concept involves creating a quantum feedback loop where continuous measurements of the system's state are processed through a quantum measurement apparatus and used to modify the control input. The utilization of quantum entanglement is explored as a mechanism to improve the sensitivity of the feedback loop, potentially leading to dramatically faster and more precise control compared to traditional approaches. This research introduces a theoretical framework and explores the mathematical foundations for such systems, highlighting key advantages and potential challenges. The presented model demonstrates the feasibility of adapting control strategies in real-time, based on quantum measurements, for improved system regulation and stability. This work represents a significant step towards exploring the application of quantum technologies in control engineering.