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Surface codes are generally studied based on the assumption that each of the qubits that make up the surface code lattice suffers noise that is independent and identically distributed (i.i.d.). However, real benchmarks of the individual…

Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our…

One of the main challenge for an efficient implementation of quantum information technologies is how to counteract quantum noise. Quantum error correcting codes are therefore of primary interest for the evolution towards quantum computing…

Quantum Physics · Physics 2023-02-28 Lorenzo Valentini , Diego Forlivesi , Marco Chiani

Fault-tolerant quantum computers will depend crucially on the performance of the classical decoding algorithm which takes in the results of measurements and outputs corrections to the errors inferred to have occurred. Machine learning…

Quantum Physics · Physics 2025-04-18 John Blue , Harshil Avlani , Zhiyang He , Liu Ziyin , Isaac L. Chuang

In this work we study the single-shot performance of higher dimensional hypergraph product codes decoded using belief-propagation and ordered-statistics decoding [Panteleev and Kalachev, 2021]. We find that decoding data qubit and syndrome…

Quantum Physics · Physics 2023-06-28 Oscar Higgott , Nikolas P. Breuckmann

Fast decoders that achieve strong error suppression are essential for fault-tolerant quantum computation (FTQC) from both practical and theoretical perspectives. The union-find (UF) decoder for the surface code is widely regarded as a…

Quantum Physics · Physics 2026-05-07 Satoshi Yoshida , Ethan Lake , Hayata Yamasaki

Quantum error correction requires accurate and efficient decoding to optimally suppress errors in the encoded information. For concatenated codes, where one code is embedded within another, optimal decoding can be achieved using a…

Quantum Physics · Physics 2025-05-07 Basudha Srivastava , Yinzi Xiao , Anton Frisk Kockum , Ben Criger , Mats Granath

Topological error correcting codes, and particularly the surface code, currently provide the most feasible roadmap towards large-scale fault-tolerant quantum computation. As such, obtaining fast and flexible decoding algorithms for these…

Quantum Physics · Physics 2021-01-12 Ryan Sweke , Markus S. Kesselring , Evert P. L. van Nieuwenburg , Jens Eisert

Color codes present distinct advantages for fault-tolerant quantum computing, such as high encoding rates and the transversal implementation of Clifford gates. However, existing matching-based decoders for the color codes such as the…

Quantum Physics · Physics 2025-11-18 Yantong Liu , Junjie Wu , Lingling Lao

Quantum technologies have the potential to solve certain computationally hard problems with polynomial or super-polynomial speedups when compared to classical methods. Unfortunately, the unstable nature of quantum information makes it prone…

Quantum error correction codes (QECCs) are critical for realizing reliable quantum computing by protecting fragile quantum states against noise and errors. However, limited research has analyzed the noise resilience of QECCs to help select…

Quantum Physics · Physics 2025-04-22 Avimita Chatterjee , Subrata Das , Swaroop Ghosh

To make practical quantum algorithms work, large-scale quantum processors protected by error-correcting codes are required to resist noise and ensure reliable computational outcomes. However, a major challenge arises from defects in…

Quantum computers require error correction to achieve universal quantum computing. However, current decoding of quantum error-correcting codes relies on classical computation, which is slower than quantum operations in superconducting…

Quantum Physics · Physics 2025-06-11 Pan Zhang

It has recently been shown that there are efficient algorithms for quantum computers to solve certain problems, such as prime factorization, which are intractable to date on classical computers. The chances for practical implementation,…

Quantum Physics · Physics 2009-10-30 Adriano Barenco , Todd A. Brun , Ruediger Schack , Tim Spiller

Errors in surface code have typically been decoded by Minimum Weight Perfect Matching (MWPM) based method. Recently, neural-network-based Machine Learning (ML) techniques have been employed for this purpose. Here we propose a two-level (low…

Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently…

Quantum Physics · Physics 2025-07-14 Pavithran Iyer , Aditya Jain , Stephen D. Bartlett , Joseph Emerson

Quantum error correction works effectively only if the error rate of gate operations is sufficiently low. However, some rare physical mechanisms can cause a temporary increase in the error rate that affects many qubits; examples include…

Quantum Physics · Physics 2024-06-28 Shi Jie Samuel Tan , Christopher A. Pattison , Matt McEwen , John Preskill

The construction of topological error correction codes requires the ability to fabricate a lattice of physical qubits embedded on a manifold with a non-trivial topology such that the quantum information is encoded in the global degrees of…

Quantum Physics · Physics 2017-10-18 James M. Auger , Hussain Anwar , Mercedes Gimeno-Segovia , Thomas M. Stace , Dan E. Browne

Demonstrating quantum supremacy, a complexity-guaranteed quantum advantage against over the best classical algorithms by using less universal quantum devices, is an important near-term milestone for quantum information processing. Here we…

Quantum Physics · Physics 2016-10-13 Keisuke Fujii

Topological codes have many desirable properties that allow fault-tolerant quantum computation with relatively low overhead. A core challenge for these codes, however, is to achieve a low-overhead universal gate set with limited…

Quantum Physics · Physics 2026-04-03 Julio C. Magdalena de la Fuente , Noa Feldman , Jens Eisert , Andreas Bauer
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