More Practical and Adaptive Algorithms for Online Quantum State Learning

1 Jun 2020Yifang ChenXin Wang

Online quantum state learning is a recently proposed problem by Aaronson et al. (2018), where the learner sequentially predicts $n$-qubit quantum states based on given measurements on states and noisy outcomes. In the previous work, the algorithms are worst-case optimal in general but fail in achieving tighter bounds in certain simpler or more practical cases... (read more)

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