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Decoding surface codes with deep reinforcement learning and probabilistic policy reuse
A significant amount of theoretical studies have provided various types of QEC codes; one of the notable topological codes is the surface code, and its features, such as the requirement of only nearest-neighboring two-qubit control gates and a large error threshold, make it a leading candidate for scalable quantum computation.
Quantum Architecture Search via Continual Reinforcement Learning
To aid this endeavor, this paper proposes a machine learning-based method to construct quantum circuit architectures.
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