High-Precision Measurement of Rydberg State Hyperfine Splitting in a Room-Temperature Vapour Cell

We present direct measurements of the hyperfine splitting of Rydberg states in rubidium 87 using Electromagnetically Induced Transparency (EIT) spectroscopy in a room-temperature vapour cell. With this method, and in spite of Doppler-broadening, line-widths of 3.7 MHz FWHM, i.e. significantly below the intermediate state natural linewidth are reached. This allows resolving hyperfine splittings for Rydberg s-states with n=20...24. With this method we are able to determine Rydberg state hyperfine splittings with an accuracy of approximately 100 kHz. Ultimately our method allows accuracies of order 5 kHz to be reached. Furthermore we present a direct measurement of hyperfine-resolved Rydberg state Stark-shifts. These results will be of great value for future experiments relying on excellent knowledge of Rydberg-state energies an

On the role of entanglement in quantum computational speed-up

For any quantum algorithm operating on pure states we prove that the presence of multi-partite entanglement, with a number of parties that increases unboundedly with input size, is necessary if the quantum algorithm is to offer an exponential speed-up over classical computation. Furthermore we prove that the algorithm can be classically efficiently simulated to within a prescribed tolerance \eta even if a suitably small amount of global entanglement (depending on \eta) is present. We explicitly identify the occurrence of increasing multi-partite entanglement in Shor's algorithm. Our results do not apply to quantum algorithms operating on mixed states in general and we discuss the suggestion that an exponential computational speed-up might be possible with mixed states in the total absence of entanglement. Finally, despite the essential role of entanglement for pure state algorithms, we argue that it is nevertheless misleading to view entanglement as a key resource for quantum computational power.Comment: Main proofs simplified. A few further explanatory remarks added. 22 pages, plain late

Beyond icosahedral symmetry in packings of proteins in spherical shells

The formation of quasi-spherical cages from protein building blocks is a remarkable self-assembly process in many natural systems, where a small number of elementary building blocks are assembled to build a highly symmetric icosahedral cage. In turn, this has inspired synthetic biologists to design de novo protein cages. We use simple models, on multiple scales, to investigate the self-assembly of a spherical cage, focusing on the regularity of the packing of protein-like objects on the surface. Using building blocks, which are able to pack with icosahedral symmetry, we examine how stable these highly symmetric structures are to perturbations that may arise from the interplay between flexibility of the interacting blocks and entropic effects. We find that, in the presence of those perturbations, icosahedral packing is not the most stable arrangement for a wide range of parameters; rather disordered structures are found to be the most stable. Our results suggest that (i) many designed, or even natural, protein cages may not be regular in the presence of those perturbations, and (ii) that optimizing those flexibilities can be a possible design strategy to obtain regular synthetic cages with full control over their surface properties.Comment: 8 pages, 5 figure

Arctic decadal variability in a warming world

Natural decadal variability of surface air temperature might obscure Arctic temperature trends induced by anthropogenic forcing. It is therefore imperative to know how Arctic decadal variability (ADV) will change as the climate warms. In this study, we evaluate ADV characteristics in three equilibrium climates with present-day, double, and quadrupled atmospheric CO2 forcing. The dominant region of variability, which is located over the Barents and Greenland Sea at present, shifts to the central Arctic and Siberian regions as the climate warms. The maximum variability in sea ice cover and surface air temperature occurs in the CO2 doubling climate when sea ice becomes more vulnerable to melt over vast stretches of the Arctic. Furthermore, the links between dominant atmospheric circulation modes and Arctic surface climate characteristics vary strongly with climate change. For instance, a positive Arctic Oscillation index is associated with a colder Arctic in warmer climates, instead of a warmer Arctic at present. Such changing relationships are partly related to the retreat of sea ice because altered wind patterns influence the sea ice distribution and hence the associated local surface fluxes. The atmospheric pressure distributions governing ADV and the associated large-scale dynamics also change with climate warming. The changing character of the ADV shows that it is vital to consider (changes in) ADV when addressing Arctic warming in climate model projections

Seasonal and regional contrasts of future trends in interannual arctic climate variability

Future changes in interannual variability (IAV) of Arctic climate indicators such as sea ice and precipitation are still fairly uncertain. Alongside global warming-induced changes in means, a thorough understanding of IAV is needed to more accurately predict sea ice variability, distinguish trends and natural variability, as well as to reduce uncertainty around the likelihood of extreme events. In this study we rank and select CMIP6 models based on their ability to replicate observations, and quantify simulated IAV trends (1981–2100) of Arctic surface air temperature, evaporation, precipitation, and sea ice concentration under continued global warming. We argue that calculating IAV on grid points before area-averaging allows for a more realistic picture of Arctic-wide changes. Large model ensembles suggest that on shorter time scales (30 years), IAV of all variables is strongly dominated by natural variability (e.g. 93% for sea ice area in March). Long-term trends of IAV are more robust, and reveal strong seasonal and regional differences in their magnitude or even sign. For example, IAV of surface temperature increases in the Central Arctic, but decreases in lower latitudes. Arctic precipitation variability increases more in summer than in winter; especially over land, where in the future it will dominantly fall as rain. Our results emphasize the need to address such seasonal and regional differences when portraying future trends of Arctic climate variability.</p