More Practical and Adaptive Algorithms for Online Quantum State Learning

Understand

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.
• In this paper, we develop algorithms to advance the online learning of quantum states.