Decoding algorithms for surface codes

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 to errors. For this reason, quantum error correction is an...

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Vydané v:Quantum (Vienna, Austria) Ročník 8; s. 1498
Hlavní autori: deMarti iOlius, Antonio, Fuentes, Patricio, Orús, Román, Crespo, Pedro M., Etxezarreta Martinez, Josu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften 10.10.2024
ISSN:2521-327X, 2521-327X
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Abstract 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 to errors. For this reason, quantum error correction is an invaluable tool to make quantum information reliable and enable the ultimate goal of fault-tolerant quantum computing. Surface codes currently stand as the most promising candidates to build near term error corrected qubits given their two-dimensional architecture, the requirement of only local operations, and high tolerance to quantum noise. Decoding algorithms are an integral component of any error correction scheme, as they are tasked with producing accurate estimates of the errors that affect quantum information, so that they can subsequently be corrected. A critical aspect of decoding algorithms is their speed, since the quantum state will suffer additional errors with the passage of time. This poses a connundrum, where decoding performance is improved at the expense of complexity and viceversa. In this review, a thorough discussion of state-of-the-art decoding algorithms for surface codes is provided. The target audience of this work are both readers with an introductory understanding of the field as well as those seeking to further their knowledge of the decoding paradigm of surface codes. We describe the core principles of these decoding methods as well as existing variants that show promise for improved results. In addition, both the decoding performance, in terms of error correction capability, and decoding complexity, are compared. A review of the existing software tools regarding surface codes decoding is also provided.
AbstractList 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 to errors. For this reason, quantum error correction is an invaluable tool to make quantum information reliable and enable the ultimate goal of fault-tolerant quantum computing. Surface codes currently stand as the most promising candidates to build near term error corrected qubits given their two-dimensional architecture, the requirement of only local operations, and high tolerance to quantum noise. Decoding algorithms are an integral component of any error correction scheme, as they are tasked with producing accurate estimates of the errors that affect quantum information, so that they can subsequently be corrected. A critical aspect of decoding algorithms is their speed, since the quantum state will suffer additional errors with the passage of time. This poses a connundrum, where decoding performance is improved at the expense of complexity and viceversa. In this review, a thorough discussion of state-of-the-art decoding algorithms for surface codes is provided. The target audience of this work are both readers with an introductory understanding of the field as well as those seeking to further their knowledge of the decoding paradigm of surface codes. We describe the core principles of these decoding methods as well as existing variants that show promise for improved results. In addition, both the decoding performance, in terms of error correction capability, and decoding complexity, are compared. A review of the existing software tools regarding surface codes decoding is also provided.
ArticleNumber 1498
Author Fuentes, Patricio
deMarti iOlius, Antonio
Crespo, Pedro M.
Etxezarreta Martinez, Josu
Orús, Román
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  surname: deMarti iOlius
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  organization: Department of Basic Sciences, Tecnun - University of Navarra, 20018 San Sebastian, Spain
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  givenname: Patricio
  surname: Fuentes
  fullname: Fuentes, Patricio
  organization: Photonic Inc., Vancouver, British Columbia, Canada
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  fullname: Orús, Román
  organization: Multiverse Computing, Pio Baroja 37, 20008 San Sebastián, Spain, Donostia International Physics Center, Paseo Manuel de Lardizabal 4, 20018 San Sebastián, Spain, IKERBASQUE, Basque Foundation for Science, Plaza Euskadi 5, 48009 Bilbao, Spain
– sequence: 4
  givenname: Pedro M.
  surname: Crespo
  fullname: Crespo, Pedro M.
  organization: Department of Basic Sciences, Tecnun - University of Navarra, 20018 San Sebastian, Spain
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  givenname: Josu
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  fullname: Etxezarreta Martinez, Josu
  organization: Department of Basic Sciences, Tecnun - University of Navarra, 20018 San Sebastian, Spain
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