Quantum Computing in the Computational Landscape of Power Electronics: Vision and Reality

TL;DR

This article investigates how quantum computing could transform power electronics by formulating offline mixed-integer optimization problems as $QUBO$ models and solving them with gate-based quantum algorithms, exemplified by a DC-DC boost converter design solved on quantum hardware using QAOA. It reviews the foundational mapping to Ising models, discusses constraint handling and practical workflow considerations, and presents a small-scale experimental demonstration that marks a symbolic milestone for the field. The paper also outlines a forward-looking vision across short-, mid-, and long-term horizons, emphasizing problem formulation, hardware progress, and cross-disciplinary collaboration with AI and digital twins to realize quantum-enabled improvements in design, control, and simulation. Overall, it argues that while current quantum hardware is nascent, early exploratory efforts and hybrid quantum–classical workflows can help power electronics communities prepare for eventual quantum advantage. The work highlights the potential for Pareto-front generation, offline optimization, and advanced pulse-pattern design, while acknowledging that real-time online control remains distant given present technology and encoding limitations.

Abstract

Quantum computing is rapidly emerging as a promising technology for solving complex optimization problems that arise in various engineering fields. Therefore, it holds significant promise to transform the computational foundations of power electronics. Motivated by this potential, this paper adopts a visionary perspective to examine how quantum computing could influence the evolution of power electronics in areas such as converter design, control, modulation, simulation workflows, and beyond. Within this framework, the current status, limitations, and anticipated progress of quantum algorithms and hardware are discussed, together with their potential to enable efficient solutions to large-scale, multiobjective, mixed-integer optimization problems. To place these developments in context, the paper begins with a concise tutorial on fundamental concepts in quantum computing, serving as both an introduction to the field and a bridge to its potential applications in power electronics. As a first step in this direction, the use of quantum computing for solving offline mixed-integer optimization problems commonly encountered in power electronics is examined. To this end, a simplified power electronics design problem is reformulated as a quadratic unconstrained binary optimization (QUBO) problem and executed on quantum hardware, despite current limitations such as low qubit counts and hardware noise. This demonstration marks a pioneering step towards leveraging quantum computing in power electronics and motivates the value of early adoption and exploration. Building on these insights, the paper outlines a forward-looking vision in which quantum computing becomes an integral part of the computational landscape of power electronics, guiding its transition from classical to quantum-enabled design and operation.

Figures (17)

• Figure 1: Schematic illustration of QAOA.
• Figure 2: Engineering workflow for implementing a constrained optimization problem of industry relevance on quantum hardware.
• Figure 3: Topology of the dc-dc boost converter.
• Figure 4: Eigenvalue distributions of the Ising formulation of problem \ref{['design_problem_QUBO']}. The inset shows the $10$ lowest-energy eigenvalues. The green star corresponds to the optimal solution of problem \ref{['design_problem']}, blue circles correspond to feasible solutions, and black crosses represent infeasible solutions.
• Figure 5: QAOA landscape of Instance 1 for $p=1$ using a $500\times 500$ grid for $\beta$ and $\gamma$ between $-\frac{\pi}{4}$ and $\frac{\pi}{4}$ (axes are truncated for legibility). The black and white circles indicate the start and end points of Adam optimizer for the initialization described in Section \ref{['subsec:setup']} and the white line its trajectory. The white star denotes the point of minimum energy observed in the grid. Gray points indicate the start point of $10$ random initializations and gray lines the trajectory that was followed by the Adam optimizer.