Its complexity constrains advancements in fusion energy and high energy applications driven by plasma physics, multiscale phenomena beyond classical computing limits. These transformative solutions, especially in plasma simulations, for which exponential speedup is possible, represent significant promise toward breakthroughs in sustainable energy and extreme state studies. In this review, Quantum Computing (QC) is explored as a means to drive plasma physics simulations forward by providing applications such as fusion energy and high-energy systems. This includes computational methods for simulating turbulence, wave-particle interactions, and Magnetohydrodynamic (MHD) instabilities that have near-quantum efficiency. We show that by integrating QC into plasma research, one can solve large-scale linear equations, compute eigenvalues, and optimize complex systems, performing better than classical methods. This discussion examines the potential of quantum computing for plasma physics, highlighting its current limitations, including hardware constraints and the need for specialized algorithms tailored to model complex plasma phenomena accurately. These challenges notwithstanding, QC has the potential to dramatically change plasma modeling and expedite the development of fusion reactors. QC represents a new approach to engineer away computational bottlenecks, providing unprecedented views of plasma behavior needed for sustainable energy breakthroughs. The results from this work underscore the continued importance of looking outside of plasma physics to realize QC’s full potential in advancing high-energy science.
Its complexity constrains advancements in fusion energy and high energy applications driven by plasma physics, multiscale phenomena beyond classical computing limits. These transformative solutions, especially in plasma simulations, for which exponential speedup is possible, represent significant promise toward breakthroughs in sustainable energy and extreme state studies. In this review, Quantum Computing (QC) is explored as a means to drive plasma physics simulations forward by providing applications such as fusion energy and high-energy systems. This includes computational methods for simulating turbulence, wave-particle interactions, and Magnetohydrodynamic (MHD) instabilities that have near-quantum efficiency. We show that by integrating QC into plasma research, one can solve large-scale linear equations, compute eigenvalues, and optimize complex systems, performing better than classical methods. This discussion examines the potential of quantum computing for plasma physics, highlighting its current limitations, including hardware constraints and the need for specialized algorithms tailored to model complex plasma phenomena accurately. These challenges notwithstanding, QC has the potential to dramatically change plasma modeling and expedite the development of fusion reactors. QC represents a new approach to engineer away computational bottlenecks, providing unprecedented views of plasma behavior needed for sustainable energy breakthroughs. The results from this work underscore the continued importance of looking outside of plasma physics to realize QC’s full potential in advancing high-energy science.
Nuclear fusion is a great promise for the route toward clean, abundant, and sustainable energy, possibly solving the limits of contemporary resources. Yet, models for controlled fusion on Earth demand so much simulation: calculations into particle interaction and plasma behavior and even reaction dynamics that defy strict classical calculation. Quantum computing-that has the ability to support those complex, multi-dimensional calculations-offers hope in the fusion research frontier. This paper introduces quantum algorithms, such as Quantum Phase Estimation for predictions of energy states, Variational Quantum Eigen solver for modeling particle interactions, and Quantum Monte Carlo to simulate stochastic processes like plasma turbulence. The algorithms together will facilitate precise modeling and optimization of the fusion parameters to maximize energy output and reactor stability. This hybrid quantum-classical approach will be: classical computers for preprocessing data and intensive simulations using quantum processors. Preliminary experimental data from IBM Quantum suggests that quantum algorithms could be more efficient and accurate than their classical counterparts. This work, therefore, demonstrates the potential that quantum computing holds to speed up research in fusion, leading to a scalable solution for high-stakes applications in energy and the scientific domain.
Plasma simulations for magnetic-confinement fusion (MCF) involve high-dimensional kinetic dynamics, strong nonlinear coupling, complex geometries, and multiscale spatiotemporal behavior, imposing severe computational bottlenecks. While quantum computing offers a potential alternative for accelerating plasma simulation problems, its practical advantage for fusion research has not yet been demonstrated. We review existing quantum computing studies across kinetic dynamics, wave interactions, equilibrium, and macroscopic stability, evaluating their hardware implementations and operational bottlenecks. Current studies remain largely restricted to linearized equations, reduced models, or isolated computational subroutines, leaving a substantial gap from realistic fusion simulations. We summarize key problem characteristics, including high dimensionality, structured dynamics, and measurement requirements, under which plasma applications are most likely to yield a quantum advantage. Guided by these considerations, we highlight potential approaches and present a roadmap toward practical quantum advantage of simulating plasma in MCF.
Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.
The rapid growth of data-intensive applications and cloud computing has significantly increased the energy consumption of modern computing infrastructures, particularly data centers, raising critical sustainability and environmental concerns. Green computing seeks to address these challenges by improving energy efficiency and reducing carbon footprints without sacrificing performance. Quantum computing has emerged as a promising paradigm due to its ability to exploit quantum mechanical principles such as superposition and entanglement to achieve computational speedups beyond classical limits. This paper examines the role of quantum computing in advancing green computing technologies by analyzing its algorithmic capabilities, application domains, and hardware-related energy implications. Key quantum algorithms, including Shor’s algorithm, Grover’s search, the Quantum Approximate Optimization Algorithm (QAOA), and quantum simulation methods, are reviewed with respect to their potential to reduce execution time and energy consumption for computationally intensive tasks such as cryptography, optimization, and system modeling. The study further explores sustainability-oriented applications, including energy systems optimization, materials discovery for renewable technologies, and quantum machine learning for resource forecasting and management. Comparative analysis indicates that quantum approaches may offer substantial energy-efficiency advantages over classical methods as hardware matures. However, significant challenges remain, including qubit decoherence, error correction overhead, cryogenic cooling requirements, and scalability limitations. The paper concludes that continued advances in hybrid quantum–classical architectures and fault-tolerant hardware are essential for realizing quantum computing’s potential contribution to sustainable and energy-efficient computing.
This is a review of recent research exploring and extending present-day quantum computing capabilities for fusion energy science applications. We begin with a brief tutorial on both ideal and open quantum dynamics, universal quantum computation, and quantum algorithms. Then, we explore the topic of using quantum computers to simulate both linear and nonlinear dynamics in greater detail. Because quantum computers can only efficiently perform linear operations on the quantum state, it is challenging to perform nonlinear operations that are generically required to describe the nonlinear differential equations of interest. In this work, we extend previous results on embedding nonlinear systems within linear systems by explicitly deriving the connection between the Koopman evolution operator, the Perron–Frobenius evolution operator, and the Koopman–von Neumann evolution (KvN) operator. We also explicitly derive the connection between the Koopman and Carleman approaches to embedding. Extension of the KvN framework to the complex-analytic setting relevant to Carleman embedding, and the proof that different choices of complex analytic reproducing kernel Hilbert spaces depend on the choice of Hilbert space metric are covered in the appendixes. Finally, we conclude with a review of recent quantum hardware implementations of algorithms on present-day quantum hardware platforms that may one day be accelerated through Hamiltonian simulation. We discuss the simulation of toy models of wave–particle interactions through the simulation of quantum maps and of wave–wave interactions important in nonlinear plasma dynamics.
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage—namely, a significant (in some cases exponential) speedup of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small-scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models that are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this Roadmap paper, led by CERN, DESY, and IBM, we provide the status of high-energy physics quantum computations and give examples of theoretical and experimental target benchmark applications, which can be addressed in the near future. Having in mind hardware with about 100 qubits capable of executing several thousand two-qubit gates, where possible, we also provide resource estimates for the examples given using error-mitigated quantum computing. The ultimate declared goal of this task force is therefore to trigger further research in the high-energy physics community to develop interesting use cases for demonstrations on near-term quantum computers.