Machine-Learning based Decoding of Surface Code Syndromes in Quantum Error Correction

Bhoumik, Debasmita; Sen, Pinaki; Majumdar, Ritajit; Sur-Kolay, Susmita; KJ, Latesh Kumar; Iyengar, S. S.

doi:10.55708/js0106004

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…A quantum circuit can be arbitrarily distributed, assuming gate teleportation between devices is possible, which is not necessarily true. Bhoumik et al [7] consider the scheduling problem regarding error mitigation by finding optimal mappings from (cut) subcircuits to multiple QPUs. Their work is based on an integer linear program, which optimizes for circuit fidelity.…”

Section: Background and Related Workmentioning

confidence: 99%

Multithreaded Parallelism for Heterogeneous Clusters of QPUs

Seitz,

Geiger,

Mendl

2024

ISC High Performance 2024 Research Paper Proceedings (39th International Conference)

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In this work, we present MILQ, a quantum unrelated parallel machines scheduler and cutter. The setting of unrelated parallel machines considers independent hardware backends, each distinguished by differing setup and processing times. MILQ optimizes the total execution time of a batch of circuits scheduled on multiple quantum devices. It leverages stateof-the-art circuit-cutting techniques to fit circuits onto the devices and schedules them based on a mixed-integer linear program. Our results show a total improvement of up to 26 % compared to a baseline approach.

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Section: Background and Related Workmentioning

confidence: 99%

Multithreaded Parallelism for Heterogeneous Clusters of QPUs

Seitz,

Geiger,

Mendl

2024

ISC High Performance 2024 Research Paper Proceedings (39th International Conference)

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show abstract

“…Various quantum error-correcting codes have been proposed and studied, of which surface codes are the main area of research today. Surface codes are a family of quantum error-correcting codes that utilize neighborhood interactions between planarly arranged physical qubits for error correction purposes [13][14][15]. However, on the dense lattice of a surface code, each qubit is connected to four other qubits, potentially causing multiple frequency collisions between them.…”

Section: Introductionmentioning

confidence: 99%

Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder

Li,

Gan

et al. 2023

Applied Sciences

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Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.

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Convolutional neural network based decoders for surface codes

Bordoni

Giagu

2023

Quantum Inf Process

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The decoding of error syndromes of surface codes with classical algorithms may slow down quantum computation. To overcome this problem it is possible to implement decoding algorithms based on artificial neural networks. This work reports a study of decoders based on convolutional neural networks, tested on different code distances and noise models. The results show that decoders based on convolutional neural networks have good performance and can adapt to different noise models. Moreover, explainable machine learning techniques have been applied to the neural network of the decoder to better understand the behaviour and errors of the algorithm, in order to produce a more robust and performing algorithm.

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Machine-Learning based Decoding of Surface Code Syndromes in Quantum Error Correction

Cited by 6 publications

References 27 publications

Multithreaded Parallelism for Heterogeneous Clusters of QPUs

Multithreaded Parallelism for Heterogeneous Clusters of QPUs

Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder

Convolutional neural network based decoders for surface codes

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