Online learning of quantum states under structure
This work investigates how incorporating structural assumptions into online learning of quantum states improves performance over general methods. By assuming measurements have low rank or using specific loss functions like squared L2, the authors achieve significantly stronger regret guarantees, reaching logarithmic regret, independent of system size.