Robustly learning the Hamiltonian dynamics of a superconducting quantum processor.

Precise means of characterizing analog quantum simulators are key to developing quantum simulators capable of beyond-classical computations. Here, we precisely estimate the free Hamiltonian parameters of a superconducting-qubit analog quantum simulator from measured time-series data on up to 14 qubits. To achieve this, we develop a scalable Hamiltonian learning algorithm that is robust against state-preparation and measurement (SPAM) errors and yields tomographic information about those SPAM errors.

Easing the Monte Carlo sign problem.

Quantum Monte Carlo (QMC) methods are the gold standard for studying equilibrium properties of quantum many-body systems. However, in many interesting situations, QMC methods are faced with a sign problem, causing the severe limitation of an exponential increase in the runtime of the QMC algorithm. In this work, we develop a systematic, generally applicable, and practically feasible methodology for easing the sign problem by efficiently computable basis changes and use it to rigorously assess the sign problem.

Serum levels of the adipokine chemerin are increased in preeclampsia during and 6 months after pregnancy.

Unlabelled: Preeclampsia is a serious cardiovascular complication in pregnancy which is associated with an increased future metabolic and cardiovascular risk for mother and newborn. Recently, chemerin was introduced as a novel adipokine inducing insulin resistance in vitro and in vivo. In the current study, we investigated serum concentrations of chemerin by ELISA in control and preeclampsia patients during pregnancy (

Control: n=37, preeclampsia: n=37) and 6 months after delivery (

Control: n=35, preeclampsia: n=36).