John Calsamiglia

The task of testing the validity of a hypothesis underlies numerous applications in quantum information theory. The most commonly investigated approach is that of gathering all the available (quantum) data and making a final decision based on a collective measurement. However, such offline strategies are often far from practical, both in the amount of data required as well as in the complexity of the required measurement. In some settings, when the goal is quick detection, offline algorithms are not applicable at all, as they can only make a decision once all samples are received. Sequential methods offer the use of online strategies, where samples are requested on a need-to-know basis, drastically reducing the number of required samples in order to guarantee the, task specific, associated performance criteria. While extensively investigated and applied in the classical setting, we know far less about the optimal performance of such online strategies when quantum data is available. In this joint submission we present major recent progress on sequential methods for the fundamental tasks of quantum state discrimination, channel discrimination and quickest change point detection. In summary, we provide a comprehensive picture of the optimal asymptotic performance of online strategies in these settings under different performance criteria.

We study the problem of estimating distance measures between unknown quantum states, given M and N copies of each type. For pure states, any distance measure can be expressed in terms of the overlap between the states. Overlap estimation is a fundamental primitive in quantum information processing that is commonly accomplished from the outcomes of N swap-tests, a joint measurement on one copy of each type whose outcome probability is a linear function of the overlap. We show that a more precise estimate can be obtained by allowing for general collective measurements on all copies, in particular using weak Schur sampling. We derive the statistics of the optimal measurement and compute the optimal mean square error in the asymptotic pointwise and finite Bayesian estimation settings. Besides, we consider two strategies relying on the estimation of one or both the states, and show that, although they are suboptimal, they outperform the swap test. In particular, the swap test is extremely inefficient for small values of the overlap, which become exponentially more likely as the dimension increases. We show that the optimal measurement is less invasive than the swap test and study the robustness to depolarizing noise for qubit states. In the case of mixed states, distance measure cannot be expressed in terms of a single distance. With the swap test, one can estimate the Hilbert-Schmidt distance and the overlap, but it is not possible to obtain other very relevant distance measures. We show that simultaneous weak Schur sampling can instead estimate the spectra of each of the states and of their convex combination, with weight given by M/M+N and N/M+N. This estimate is much more informative than the outcome of swap tests and it can be used to estimate a wider class of properties and distinguishability measures. In particular we show how to measure the Holevo quantity of a an ensemble constructed with the states. We discuss how to get bounds on the trace distance using these estimates, and apply these bounds to closeness and remoteness testing, improving sample complexity with respect to the swap test.