Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

Sweke, Ryan; Kesselring, Markus S.; van Nieuwenburg, Evert P. L.; Eisert, Jens

doi:10.48550/arxiv.1810.07207

Cited by 25 publications

(31 citation statements)

References 53 publications

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“…Other than this, look-up table [27] and machine learning (ML) based decoders have been proposed for surface codes as well [28][29][30]. However, for this study, we stick with the MWPM decoder.…”

Section: Surface Codesmentioning

confidence: 99%

Surface Code Design for Asymmetric Error Channels

Azad,

Lipińska,

Mahato

et al. 2021

Preprint

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Surface codes are quantum error correcting codes normally defined on 2D arrays of qubits. In this paper, we introduce a surface code design based on the fact that the severity of bit flip and phase flip errors in the physical quantum systems is asymmetric. For our proposed surface code design for asymmetric error channels, we present pseudo-threshold and threshold values in the presence of various degrees of asymmetry of Pauli X, Ŷ , and Ẑ errors in a depolarization channel. We show that, compared to symmetric surface codes, our asymmetric surface codes can provide almost double the pseudo-threshold rates while requiring less than half the number of physical qubits in the presence of increasing asymmetry in the error channel. We also demonstrate that as the asymmetry of the surface code increases, the advantage in the pseudo-threshold rates begins to saturate for any degree of asymmetry in the channel.

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Section: Surface Codesmentioning

confidence: 99%

Surface Code Design for Asymmetric Error Channels

Azad,

Lipińska,

Mahato

et al. 2021

Preprint

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“…The most popular decoding algorithm is Blossom Decoder [18] which uses the Minimum Weight Perfect Matching (MWPM) algorithm, where the decoding time increases as O(N 4 ) where N is the number of qubits. Recently machine learning (ML) is being used for decoding purposes [19,20,21,22] where, once the system is trained, the decoding technically runs as O(1). Moreover, MWPM works satisfactorily when the error probability of the system is low, as it always tries to find the minimum number of errors that can lead to the syndrome (discussed in detail in the Section II) at hand.…”

Section: Introductionmentioning

confidence: 99%

Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing

Bhoumik¹,

Sen²,

Majumdar³

et al. 2021

Preprint

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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 and high) ML-based decoding scheme, where the first level corrects errors on physical qubits and the second one corrects any existing logical errors, for different noise models. Our results show that our proposed decoding method achieves ∼ 10× and ∼ 2× higher values of pseudo-threshold and threshold respectively, than for MWPM. We show that usage of more sophisticated ML models with higher training/testing time, do not provide significant improvement in the decoder performance. Finally, data generation for training the ML decoder requires significant overhead hence lower volume of training data is desirable. We have shown that our decoder maintains a good performance with the train-test-ratio as low as 40 : 60.

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“…On the other hand, RL has been successfully applied to challenging problems in quantum physics including quantum state preparation [39,40], quantum optimal control [41,42], and quantum error correction [43,44].…”

Section: Introductionmentioning

confidence: 99%

Universal and optimal coin sequences for high entanglement generation in 1D discrete time quantum walks

Gratsea,

Metz,

Busch

2020

Preprint

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Entanglement is a key resource in many quantum information applications and achieving high values independently of the initial conditions is an important task. Here we address the problem of generating highly entangled states in a discrete time quantum walk irrespective of the initial state using two different approaches. First, we present and analyze a deterministic sequence of coin operators which produces high values of entanglement in a universal manner for a class of localized initial states. In a second approach, we directly optimize the sequence of coin operators using a reinforcement learning algorithm. While the amount of entanglement produced by the deterministic sequence is fully independent of the initial states considered, the optimized sequences achieve in general higher average values of entanglement that do however depend on the initial state parameters. Our proposed sequence and optimization algorithm are especially useful in cases where the initial state is not fully known or entanglement has to be generated in a universal manner for a range of initial states.

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Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

Cited by 25 publications

References 53 publications

Surface Code Design for Asymmetric Error Channels

Surface Code Design for Asymmetric Error Channels

Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing

Universal and optimal coin sequences for high entanglement generation in 1D discrete time quantum walks

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