Quantum Computation and Quantum Systems

Yousu Chen, Samuel Stein, Ang Li +1 more

With the global trend of pursuing clean energy and decarbonization, power systems have been evolving in a fast pace that we have never seen in the history of electrifi-cation. Power system researchers have been leveraging classical high-performance computing (HPC) techniques to increase the computational speed for better decision support. However, there are intractable problems that cannot be handled by classical HPC. Quantum computing (QC) as an emerging technology has been well recognized and holds promises to significantly revolutionize computing for complex power system applications. This paper introduces the fundamentals of quantum computing, briefly summarizes the existing quantum computing applications in literature, and explains the need of exploring quantum computing potentials for power systems. A quantum neural network application in power system contingency analysis is presented as an example to show the potential of quantum computing. More importantly, this paper emphasizes the need for co-existence of classical HPC and quantum computing and recommends three quantum computing research directions in power systems: quantum-inspired algorithm for large-scale optimization, variational quantum algorithms (VQA) translation, and power system data qubitization.

Stanley Gudder

Craig S. Lent

Minh Thanh Chung, Yaknan John Gambo, Burak Mete +12 more

Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed to provide structured training in the experimental analysis of quantum computing systems. Q-BEAST offers a curriculum that combines foundational concepts in quantum computing with practical methodologies and use cases for benchmarking and performance evaluation on actual quantum systems. Through theoretical instruction and handson experimentation, students gain experience in assessing the advantages and limitations of real quantum technologies. With that, Q-BEAST supports the education of a future generation of quantum computing users and developers. Furthermore, it also explicitly promotes a deeper integration of High Performance Computing (HPC) and QC in research and education.

Rahul Mishra

Quantum mechanics (QM) forms the basis for quantum computation (QC), a paradigm that provides distinctive problem-solving abilities. Although quantum computing (QC) has demonstrated potential in theoretical physics and computer science, its practical applications, particularly in power systems, have not been thoroughly studied. The objective of this work is to highlight the use of quantum computing (QC) as a solution for addressing the rising computational difficulties in power systems. The text provides an introduction to the historical history and essential concepts of QC, and then explores its modern applications in solving intricate power system problems. The aforementioned subjects comprise the study of alternating current (AC) and direct current (DC)” power flow, contingency analysis, state estimation, modeling of electromagnetic transients, fault diagnostics, unit commitment, and challenges related to facility location-allocation.” Despite the existing limits of noisy intermediate-scale quantum (NISQ) devices, the potential of quantum computing to address significant power system concerns on a wide scale holds promise, mostly driven by anticipated advancements in technology and increased research endeavors.

Stephen Fenner