Songqinghao Yang, Christopher K. Long, Rubén M. Otxoa, Prakash Murali, Crispin H. W. Barnes, and David R. M. Arvidsson-Shukur
Preprint published: 27 May 2026
DOI: 10.48550/arXiv.2605.28936
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Abstract
We present a resource analysis for generating high-fidelity logical magic states on silicon spin-qubit platforms. We consider a range of architectures, including a shuttling-based SpinBus design, a dense nearest-neighbor layout, and a hybrid scheme with shuttling-connected patches. We compare surface, color, and biased error-correcting codes, and analyze the and magic-state distillation protocols. Our approach combines bottom-up and top-down methodologies. We construct a hardware-level noise model based on a silicon-processor Hamiltonian with realistic parameters and non-Markovian noise, enabling estimation of physical resources required to reach target logical error rates. These results are propagated to system-level overheads for applications including spin dynamics, integer factorization, and quantum chemistry. Conversely, we fix target logical fidelities and derive corresponding constraints on hardware performance. Our framework enables systematic evaluation of resource-reduction strategies. We find that optimized control pulses reduce magic-state distillation overhead by compared to standard gate implementations. In addition, silicon-tailored biased error-correcting codes achieve an approximately threefold reduction in physical footprint relative to the surface code, even without physical-bias-preserving operations.
Citation
Songqinghao Yang, Christopher K. Long, Rubén M. Otxoa, Prakash Murali, Crispin H. W. Barnes, and David R. M. Arvidsson-Shukur. Hardware-tailored resource estimation for magic-state distillation on silicon spin qubits, 2026, arXiv:2605.28936 [quant-ph].
BibTeX
@misc{yang2026hardwaretailoredresourceestimationmagicstate,
title={Hardware-Tailored Resource Estimation for Magic-State Distillation on Silicon Spin Qubits},
author={Songqinghao Yang and Christopher K. Long and Rubén M. Otxoa and Prakash Murali and Crispin H. W. Barnes and David R. M. Arvidsson-Shukur},
year={2026},
eprint={2605.28936},
archivePrefix={arXiv},
primaryClass={quant-ph},
url={https://arxiv.org/abs/2605.28936},
}Software
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Thread
In our recent arXiv (arxiv.org/abs/2605.28936) we benchmarked QEC and magic state factories for spin qubits. By computing magic state production overheads as a function of initialization, gate, measurement, and decoherence times we estimated the runtimes for typical algorithms—see Fig.
— Christopher K. Long (@christopher-k-long.bsky.social) 11 June 2026 at 13:46
[image or embed]My favourite result was showing pulse optimization can cut magic state production overheads by 42%.
— Christopher K. Long (@christopher-k-long.bsky.social) 11 June 2026 at 13:46
In our recent arXiv (https://lnkd.in/e3erv9AE) Peter Yang, I, Rubén Miguel Otxoa de Zuazola, Prakash Murali, Prof. Crispin H. W. Barnes, and David Arvidsson-Shukur benchmarked QEC and magic state factories for spin qubits. By computing magic state production overheads as a function of initialization, gate, measurement, and decoherence times we estimated the runtimes for typical algorithms. My favourite result was showing pulse optimization can cut magic state production overheads by 42%.
— Christopher K. Long (LinkedIn) 12 June 2026
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Thread
In our recent arXiv (https://t.co/TACezZ3dDs) we benchmarked QEC and magic state factories for spin qubits. By computing magic state production overheads as a function of initialization, gate, measurement, and decoherence times we estimated the runtimes for typical algorithms. pic.twitter.com/DplzqsTWVC
— Chris Long (@Chris_K_Long45) June 11, 2026My favourite result was showing pulse optimization can cut magic state production overheads by 42%.
— Chris Long (@Chris_K_Long45) June 11, 2026