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Human Biology

Tools to Design Algorithms for Implementing Control Over Quantum Computers

Shilpa Narasimhan, Jihan Abou Halloun, Kip Nieman, Helen Durand

Published Aug 21, 2025• 0 citations
OpenAlex

Abstract

Quantum computers (QCs) may find future applications within control systems that operate manufacturing processes. For application within control engineering, quantum algorithm development must be led by control engineers. However, control engineers may face challenges in designing quantum algorithms for control engineering problems. In this work, we provide several path-finding studies that leverage engineering tools such as optimization, encryption, and computational "short-cuts" toward making algorithm design for QC easier for control engineers.

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Quantum computers (QCs) may find future applications within control systems that operate manufacturing processes. For application within control engineering, quantum algorithm development must be led by control engineers. However, control engineers may face challenges in designing quantum algorithms for control engineering problems. In this work, we provide several path-finding studies that leverage engineering tools such as optimization, encryption, and computational "short-cuts" toward making algorithm design for QC easier for control engineers.
Quantum computers (QCs) may find future applications within control systems that operate manufacturing processes. For application within control engineering, quantum algorithm development must be led by control engineers. However, control engineers may face challenges in designing quantum algorithms for control engineering problems. In this work, we provide several path-finding studies that leverage engineering tools such as optimization, encryption, and computational "short-cuts" toward making algorithm design for QC easier for control engineers.
Quantum computing has emerged as a revolutionary technology, with the potential to solve complex computational problems far more efficiently than classical computing. In recent years, quantum computing has started to make significant strides in the field of control systems, where it holds the promise of enhancing computational power, optimization, and system design. This paper explores the application of quantum computing in control systems, specifically in areas such as optimization, state estimation, and dynamic system control. We discuss how quantum algorithms can be applied to improve the efficiency of control system design, the potential benefits of quantum-enhanced control systems, and the challenges that need to be addressed before full-scale implementation. The paper also provides insights into future research directions and the integration of quantum computing into control systems.
Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of Python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the QC software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of key features including a flexible optimization toolkit, engineering-inspired filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.
Abstract Quantum computing has generated splashy headlines with pronouncements of “quantum supremacy”, but the reality is much more complicated than simple headlines can capture. Quantum computers are designed to use the interactions between quantum objects, such as electron spins, to perform useful computations. This paper will provide an overview of some of the engineering challenges involved in developing quantum computers and highlight some potential applications of interest to the mechanical engineering community. These challenges include the need to isolate the quantum bits — the logic elements used in quantum computers — from the environment to protect them from external sources of noise. In some quantum computer designs, this requires cooling the quantum bits to temperatures below 1 degree Kelvin, which requires specialized refrigeration systems. Potential future applications of quantum computers, particularly for mechanical engineers, rely on the promise of increased computational power for solving complex problems that have many degrees of freedom. This includes solving large systems of equations for finite element analysis and computational fluid dynamics, and in computational chemistry for materials optimization. Quantum computing is clearly a future technology — more development is needed before it will be used for engineering applications. That said, there are clear potential applications in mechanical engineering, and it will be a future technology that will be used for engineering analysis-based design optimization, digital twin development, and other relevant applications.
Abstract Quantum computing as the emerging paradigm for scientific computing has attracted significant research attention in the past decade. Quantum algorithms to solve the problems of linear systems, eigenvalue, optimization, machine learning, and others have been developed. The main advantage of utilizing quantum computer to solve optimization problems is that quantum superposition allows for massive parallel searching of solutions. This article provides an overview of fundamental quantum algorithms that can be utilized in solving optimization problems, including Grover search, quantum phase estimation, quantum annealing, quantum approximate optimization algorithm, variational quantum eigensolver, and quantum walk. A review of recent applications of quantum optimization methods for engineering design, including materials design and topology optimization, is also given. The challenges to develop scalable and reliable quantum algorithms for engineering optimization are discussed.