We study the performance of efficient quantum state tomography methods based on neural network quantum states using measured data from a two-photon experiment. Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. While the power of these methods has been demonstrated on synthetic data, applications to real experimental data remain scarce. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states, i.e. to positive semi-definite density matrices, greatly improves the quality of the reconstructed states but renders the learning procedure more demanding. Including additional, possibly unjustified, constraints, such as assuming pure states, facilitates learning, but also biases the estimator.

Research on the extracellular hemeprotein ligninases of Phanerochaete chrysosporium has been hampered by the necessity to produce them in stationary culture. This investigation examined the effects of detergents on development of ligninase activity in agitated submerged cultures. Results show that addition of Tween 80, Tween 20, or 3-[(3-colamidopropyl)dimethylammonio]l-propanesulfonate to the cultures permits development of ligninase activity comparable to that routinely obtained in stationary cultures. The detergent-amended cultures express the entire ligninolytic system, assayed as the complete oxidation of [14C]lignin to '4CO2. The detergent effect is evidently not merely in facilitating release of extant enzyme. Development of ligninolytic activity in the agitated cultures, as in stationary cultures, is idiophasic. Ion-exchange fast protein-liquid chromatography indicated that the heme protein profiles in agitated and stationary cultures are very similar. These findings should make it possible to scale up production of ligninolytic enzymes in stirred tank fermentors.

No abstract

No abstract

No abstract