Online learning of quantum states**This article is an updated version of: Aaronson S, Chen X, Hazan E, Kale S and Nayak A 2018 Online learning of quantum states Advances in Neural Information Processing Systems 31 ed S Bengio, H Wallach, H Larochelle, K Grauman, N Cesa-Bianchi and R Garnett (Red Hook, NY: Curran Associates, Inc.) pp 8962–72.

Abstract

Listen

Translate article

Suppose we have many copies of an unknown n-qubit state . We measure some copies of using a known two-outcome measurement E1, then other copies using a measurement E2, and so on. At each stage t, we generate a current hypothesis about the state , using the outcomes of the previous measurements. We show that it is possible to do this in a way that guarantees that , the error in our prediction for the next measurement, is at least at most times. Even in the ‘non-realizable’ setting—where there could be arbitrary noise in the measurement outcomes—we show how to output hypothesis states that incur at most excess loss over the best possible state on the first T measurements. These results generalize a 2007 theorem by Aaronson on the PAC-learnability of quantum states, to the online and regret-minimization settings. We give three different ways to prove our results—using convex optimization, quantum postselection, and sequential fat-shattering dimension—which have different advantages in terms of parameters and portability.