Energetics of Fault-Tolerant Spin-Qubit Quantum Computation
Abstract
Semiconductor spin qubits offer a promising route to compact and scalable quantum computers, combining long coherence times in isotopically purified silicon with compatibility with semiconductor manufacturing. Beyond computational speedup, their energy efficiency is an important consideration for future large-scale systems. We evaluate the power and energy requirements of a Loss-DiVincenzo spin-qubit quantum computer by analyzing the Quantum Fourier Transform (QFT) and benchmarking it against the classical Fast Fourier Transform (FFT). We develop a detailed energy budget, identifying cryogenic cooling as the dominant baseline contribution before quantum error correction (QEC). The comparison between the QFT and the classical FFT reveals that, for any quantum workload operating under a polynomially bounded sampling regime, the crossover in energy consumption occurs at smaller problem sizes than the crossover in execution time. Thus, quantum computing can become energetically competitive before achieving a speedup in execution time. We then incorporate surface-code QEC and analyze the energy costs of QEC cycles, gate synthesis, and decoding. At scale, instrumentation and decoding become dominant contributions, while cryogenic cooling imposes a constraint on scalability. We show that optimizing the control-signal demultiplexing ratio can reduce the overall energy cost by approximately two orders of magnitude. For a system with about one million physical qubits, we estimate a power consumption of approximately 100 kW, at least one order of magnitude below that of today's highest-performance classical computing systems.
Related
- Fault-tolerant quantum computation with static atomic buses
- Local decoders for fault-tolerant quantum computation and translation-invariant stabilizer codes
- Purely-logarithmic-time- and constant-space-overhead fault-tolerant quantum computation
- Towards fault-tolerant quantum computation with universal continuous-variable gates
- Signal-to-noise problems in fault-tolerant quantum computation
BibTeX
@misc{santos2026energetics,
title = {{Energetics of Fault-Tolerant Spin-Qubit Quantum Computation}},
author = {João Santos and Jayshankar Nath and Marco Pezzutto and Valentin Savin and Tristan Meunier and Yasser Omar},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.22199},
eprint = {2610.22199},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.22199},
}
RIS
TY - UNPB
TI - Energetics of Fault-Tolerant Spin-Qubit Quantum Computation
AU - João Santos
AU - Jayshankar Nath
AU - Marco Pezzutto
AU - Valentin Savin
AU - Tristan Meunier
AU - Yasser Omar
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.22199
UR - https://arxiv.org/abs/2610.22199
AB - Semiconductor spin qubits offer a promising route to compact and scalable quantum computers, combining long coherence times in isotopically purified silicon with compatibility with semiconductor manufacturing. Beyond computational speedup, their energy efficiency is an important consideration for future large-scale systems. We evaluate the power and energy requirements of a Loss-DiVincenzo spin-qubit quantum computer by analyzing the Quantum Fourier Transform (QFT) and benchmarking it against the classical Fast Fourier Transform (FFT). We develop a detailed energy budget, identifying cryogenic cooling as the dominant baseline contribution before quantum error correction (QEC). The comparison between the QFT and the classical FFT reveals that, for any quantum workload operating under a polynomially bounded sampling regime, the crossover in energy consumption occurs at smaller problem sizes than the crossover in execution time. Thus, quantum computing can become energetically competitive before achieving a speedup in execution time. We then incorporate surface-code QEC and analyze the energy costs of QEC cycles, gate synthesis, and decoding. At scale, instrumentation and decoding become dominant contributions, while cryogenic cooling imposes a constraint on scalability. We show that optimizing the control-signal demultiplexing ratio can reduce the overall energy cost by approximately two orders of magnitude. For a system with about one million physical qubits, we estimate a power consumption of approximately 100 kW, at least one order of magnitude below that of today's highest-performance classical computing systems.
ER -