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arXiv:2502.11728 · IEEE Transactions on Quantum Engineering

Matrix Low-Dimensional Qubit Casting Based Quantum Electromagnetic Transient Network Simulation Program

Qi Lou, Yijun Xu, Wei Gu

Published Jan 1, 2026• 1 citation
arXivHTMLPDF

Abstract

In modern power systems, the integration of converter-interfaced generations requires the development of electromagnetic transient network simulation programs (EMTP) that can capture rapid fluctuations. However, as the power system scales, the EMTP's computing complexity increases exponentially, leading to a curse of dimensionality that hinders its practical application. Facing this challenge, quantum computing offers a promising approach for achieving exponential acceleration. To realize this in noisy intermediate-scale quantum computers, the variational quantum linear solution (VQLS) was advocated because of its robustness against depolarizing noise. However, it suffers data inflation issues in its preprocessing phase, and no prior research has applied quantum computing to high-frequency switching EMT networks.To address these issues, this paper first designs the matrix low-dimension qubit casting (MLQC) method to address the data inflation problem in the preprocessing of the admittance matrix for VQLS in EMT networks. Besides, we propose a real-only quantum circuit reduction method tailored to the characteristics of the EMT network admittance matrices. Finally, the proposed quantum EMTP algorithm (QEMTP) has been successfully verified for EMT networks containing a large number of high-frequency switching elements.

DOI
10.1109/tqe.2026.3712782

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In modern power systems, the integration of converter-interfaced generations requires the development of electromagnetic transient network simulation programs (EMTP) that can capture rapid fluctuations. However, as the power system scales, the EMTP's computing complexity increases exponentially, leading to a curse of dimensionality that hinders its practical application. Facing this challenge, quantum computing offers a promising approach for achieving exponential acceleration. To realize this in noisy intermediate-scale quantum computers, the variational quantum linear solution (VQLS) was advocated because of its robustness against depolarizing noise. However, it suffers data inflation issues in its preprocessing phase, and no prior research has applied quantum computing to high-frequency switching EMT networks.To address these issues, this paper first designs the matrix low-dimension qubit casting (MLQC) method to address the data inflation problem in the preprocessing of the admittance matrix for VQLS in EMT networks. Besides, we propose a real-only quantum circuit reduction method tailored to the characteristics of the EMT network admittance matrices. Finally, the proposed quantum EMTP algorithm (QEMTP) has been successfully verified for EMT networks containing a large number of high-frequency switching elements.
Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP’s computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today’s noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices–which appears to be decades away– hinder practical applications of current QEMTP methods. This paper devises a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) design of shallow-depth QEMTP quantum circuits for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTP’s performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.
This letter devises a quantum electromagnetic transients program (QEMTP) which is the first attempt to tackle the computational challenges in solving EMTP through quantum computing. The main contributions lie in: (1) A quantum-enabled EMTP formulation with Dommel's model encoded in quantum states; 2) An Harrow-Hassidim-Lloyd (HHL)-based QEMTP to solve the discrete-time nodal equations; 3) An iteration-based HHL revision for mitigating temporal errors to achieve high accuracy of QEMTP with limited quantum resources. Case studies verify the correctness and efficacy of QEMTP in simulating both fast and slow dynamics.
The advent of quantum computing has heralded unprecedented possibilities in diverse scientific domains, including electrical engineering. This research paper delves into the innovative integration of quantum computing methodologies for the time-domain simulation of electromagnetic transients in interconnected power systems. Electromagnetic transients are pivotal phenomena that influence the stability, reliability, and efficiency of power systems, necessitating accurate and rapid simulation techniques. Classical computational paradigms, albeit powerful, encounter substantial limitations in terms of computational speed and capacity when dealing with large-scale, complex interconnected power networks. To address these challenges, this paper introduces quantum algorithms that leverage the principles of superposition and entanglement, ensuring a quantum leap in simulation capabilities. A comprehensive comparison with conventional simulation methodologies is presented, highlighting the quantum algorithms' superior efficiency and precision. The quantum circuit models for various power system components are meticulously constructed and optimized for quantum resource utilization. Furthermore, the paper explores error mitigation strategies and quantum error correction codes tailored for power system applications, ensuring robustness in the presence of quantum noise and decoherence. The empirical results, obtained from simulations on quantum processors and simulators, underscore the substantial advantages and potential of quantum computing in revolutionizing electromagnetic transient analysis. This research not only paves the way for accelerated and accurate simulations but also contributes to the enhanced stability and reliability of modern interconnected power systems.
This work develops simulation methods that enable the application of the variational quantum linear solver (VQLS) to simulate quantum transport in nanoscale semiconductor devices. Most previous work on VQLS applications in semiconductor device simulations focuses on solving the Poisson equation, where the coefficient matrix of the sparse linear system is real and symmetric. Solving the quantum transport equation, however, leads to coefficient matrices that are complex and non-symmetric. This work addresses the challenges of applying VQLS to quantum transport simulations. We propose new forms of cost functions to solve complex and non-symmetric linear systems with faster computing speed. We further develop efficient decomposition methods for cost function evaluation, which target reducing the quantum circuit complexity and improving noise robustness when solving the quantum transport equation using the nonequilibrium Green's function method. While classical computation faces the challenge of the “curse of dimensionality” as the spatial-energy numerical grid dimensions grow, the proposed quantum-computing-based method scales logarithmically with the grid size, which offers a promising opportunity for addressing the computational challenges of solving quantum transport in semiconductor devices.
Quantum mechanics (QM) can be understood as a set of rules that forms the basis for developing all quantum theories. One of these theories is quantum computation (QC), i.e., computation based on QM logic. It is believed that QC provides paths to the problem solution that may not be possible for classical computers. Therefore, it has received attention to solve complex computational problems in different areas. Most of the research efforts, however, have concentrated on problems in theoretical physics and computer science, leaving little attention to solve practical problems in industrial applications. This is particularly true in power system applications where QC is mostly unknown. This paper mainly aims to attract the attention of power system researchers/engineers to QC as a potential solution to address emerging computational challenges of power systems. To this end, the historical development of QC and its fundamental concepts are first described. Then, recent contributions to solving computationally-demanding power system problems such as AC and DC power flow (PF), contingency analysis, state estimation, electromagnetic transients simulation (EMT), fault diagnosis, unit commitment (UC), and facility location–allocation (FLA) problems are discussed. Unfortunately, power system researchers have not yet been able to convincingly demonstrate a quantum advantage in solving large-scale power system problems mainly because we are in the noisy intermediate-scale quantum (NISQ) era, where quantum devices are noisy and have limited quantum resources. However, it may be demonstrated in the future with technological advances and increased research efforts in the area.
Quantum power system state estimation (QPSSE) offers an inspiring direction for tackling the challenge of state estimation through quantum computing. Nevertheless, the current bottlenecks originate from the scarcity of practical and scalable QPSSE methodologies in the noisy intermediate-scale quantum (NISQ) era. This paper devises a NISQ-QPSSE algorithm that facilitates state estimation on real NISQ devices. Our new contributions include: (1) A variational quantum circuit (VQC)-based QPSSE formulation that empowers QPSSE analysis utilizing shallow-depth quantum circuits; (2) A variational quantum linear solver (VQLS)-based QPSSE solver integrating QPSSE iterations with VQC optimization; (3) An advanced NISQ-compatible QPSSE methodology for tackling the measurement and coefficient matrix issues on real quantum computers; (4) A noise-resilient method to alleviate the detrimental effects of noise disturbances. The encouraging test results on the simulator and real-scale systems affirm the precision, universality, and scalability of our QPSSE algorithm and demonstrate the vast potential of QPSSE in the thriving NISQ era.