Quantum Computing for Fusion Energy Science Applications
I. Joseph, Y. Shi, M. D. Porter, A. R. Castelli, V. I. Geyko, F. R., Graziani, S. B. Libby, J. L. DuBois

TL;DR
This review explores how quantum computing can be applied to fusion energy science, focusing on simulating nonlinear dynamics and embedding nonlinear systems within linear quantum frameworks.
Contribution
It extends previous methods by explicitly connecting Koopman, Perron-Frobenius, and KvN operators, and discusses quantum hardware implementations for simulating nonlinear plasma dynamics.
Findings
Derived connections between Koopman, Perron-Frobenius, and KvN operators.
Extended KvN framework to complex-analytic settings for Carleman embedding.
Reviewed quantum hardware implementations for simulating nonlinear plasma models.
Abstract
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…
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Taxonomy
TopicsModel Reduction and Neural Networks · Neural Networks and Reservoir Computing · Quantum Computing Algorithms and Architecture
