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Machine learning quantum error correction codes: learning the toric code

Rodríguez Fernández, Carlos Gustavo

Dec 14, 20180 citations

Distributed Training for Deep Reinforcement Learning Decoders on the Toric Code

Adam Olsson, Gabriel Lindeby

Jan 1, 20201 citation

Adaptive Reinforcement Learning for Autonomous Quantum Error Correction in Surface Codes

Sami Rashid Mohammed Shibah

Jan 1, 20250 citations

Reinforcement Learning-based Adaptive Error Correction for BB84 Quantum Key Distribution: A Q-Learning Approach to Post-Processing Optimization

Devishree Rohit Naidu, Sweta Jain, Ananya Anand +2 more

International Conference on Connected Intelligence for Industrial Applications (CI2A)Apr 3, 20260 citations

Determining quantum topological semion code decoder performance and error correction effectiveness with reinforcement learning

Haowen Wang, Qian Cao, Yun-Jia Xue +4 more

Quantum error correction technology is a vital method to eliminate noise during the operation of quantum computers. To solve the problem caused by noise, in this paper, reinforcement learning is used to encode defects of Semion codes, and the experience replay technique is used to realize the design of decoder. Semion codes are quantum topological error correction codes with the same symmetry group Z 2 as Kitaev toric codes, we used the topological characteristics of error correction codes to map qubits to multi-dimensional space, and error correction accuracy of the decoder is calculated to be 77.5%. Calculate the threshold of topological quantum Semion code, depending on the code distance, resulting in different thresholds, p threshold = 0.081574 when the code distance is d = 3, 5, 7 and threshold p threshold = 0.09542 when the code distance is d = 5, 7, 9. And we design the Q -network to optimize the cost of quantum circuit gates and compare the size of the cost reduction under different thresholds. Reinforcement learning is an important method for designing Semion code decoders and optimizing numerical values, providing more general error models and error correction codes for future machine engineering decoders.Quantum error correction technology is a vital method to eliminate noise during the operation of quantum computers. To solve the problem caused by noise, in this paper, reinforcement learning is used to encode defects of Semion codes, and the experience replay technique is used to realize the design of decoder. Semion codes are quantum topological error correction codes with the same symmetry group Z 2 as Kitaev toric codes, we used the topological characteristics of error correction codes to map qubits to multi-dimensional space, and error correction accuracy of the decoder is calculated to be 77.5%. Calculate the threshold of topological quantum Semion code, depending on the code distance, resulting in different thresholds, p threshold = 0.081574 when the code distance is d = 3, 5, 7 and threshold p threshold = 0.09542 when the code distance is d = 5, 7, 9. And we design the Q -network to optimize the cost of quantum circuit gates and compare the size of the cost reduction under different thresholds. Reinforcement learning is an important method for designing Semion code decoders and optimizing numerical values, providing more general error models and error correction codes for future machine engineering decoders.

Frontiers in PhysicsAug 15, 20226 citations

Deep Q-learning decoder for depolarizing noise on the toric code

David Fitzek, Mattias Eliasson, Anton Frisk Kockum +1 more

This paper presents an artificial intelligence-based decoder for quantum error correction of the toric code. A neural network is trained, using reinforcement learning, to suggest error-correcting operations on the physical qubits to best avoid logical errors. By learning to take advantage of the correlations between bit-flip and phase-flip errors, it can outperform the standard matching decoder for depolarizing noise.

arXiv:1912.12919Physical Review ResearchMay 26, 202038 citations

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