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arxiv: 2601.03141 · v2 · pith:46LVXMR6new · submitted 2026-01-06 · 🪐 quant-ph

Energetics of Rydberg-atom Quantum Computing

classification 🪐 quant-ph
keywords quantumenergyrydbergfourierplatformtransformcomputercost
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While extensive research over the past decades has been dedicated to developing scalable quantum computers, the question of their energetic performance has only gained attention more recently, but its importance is now recognized. In fact, quantum computers can only be a viable alternative if their energy cost scales favorably, and some research has shown that there is even a potential quantum energy advantage. In parallel, Rydberg atoms have recently emerged as one of the most promising platforms to implement a large-scale quantum computer. This work aims at contributing first steps to understand the energy efficiency of this platform, by investigating the energy consumption of the different elements of a Rydberg atom quantum computer. First, an experimental implementation of the Quantum Phase Estimation algorithm is analyzed, and an estimation of the energetic cost of executing it is calculated. Then, we derive an estimate of how the energy cost of performing the Quantum Fourier Transform scales with the number of qubits in the Rydberg platform. This analysis facilitates a comparison of the energy consumption of different elements within a Rydberg atom quantum computer, from the preparation of the atoms to the execution of the algorithm, and the measurement of the final state, enabling the evaluation of the energy expenditure of the Rydberg platform and the identification of potential improvements. Finally, we use the Quantum Fourier Transform as an energetic benchmark, comparing the scaling we obtained to that of the execution of the Discrete Fourier Transform in two state-of-the-art classical supercomputers. This comparison indicates that, in an ideal error-free scenario, a quantum energy advantage is achieved in the Rydberg platform for the Fourier Transform, in a regime where classical algorithms are still faster.

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