Logical qubit encoding and quantum error correction (QEC) have been experimentally demonstrated in various physical systems with multiple physical qubits, however, logical operations are challenging due to the necessary nonlocal operations. Alternatively, logical qubits with bosonic-mode-encoding are of particular interest because their QEC protection is hardware efficient, but gate operations on QEC protected logical qubits remain elusive. Here, we experimentally demonstrate full control on a single logical qubit with a binomial bosonic code, including encoding, decoding, repetitive QEC, and high-fidelity (97.0% process fidelity on average) universal quantum gate set on the logical qubit. The protected logical qubit has shown 2.8 times longer lifetime than the uncorrected one. A Ramsey experiment on a protected logical qubit is demonstrated for the first time with two times longer coherence than the unprotected one. Our experiment represents an important step towards fault-tolerant quantum computation based on bosonic encoding.

Summary Inhibition of DAF-2 (IGF-1 receptor) or RSKS-1 (S6K), key molecules in the insulin/IGF-1 signaling (IIS) and target of rapamycin (TOR) pathways respectively, extends lifespan in C. elegans. However it has not been clear how they interact with each other and in which tissues to modulate longevity. Here we demonstrate that mutations in daf-2 and rsks-1 when combined produce a nearly five-fold increase in longevity that is much greater than the sum of single mutations. This synergistic lifespan extension requires positive feedback regulation of DAF-16 (FOXO) via the AMP-activated protein kinase (AMPK) complex. We further identify germ line as the key tissue for the synergistic longevity. Moreover, germline-specific inhibition of rsks-1 activates DAF-16 in the intestine. Together, our findings highlight the importance of the germ line in significantly prolonged longevity by daf-2 rsks-1, which provides important implications for interactions between the two major conserved longevity pathways in more complex organisms.

A quantum version of generative adversarial learning is experimentally demonstrated with a superconducting circuit.

The main difference between multi-volume rendering and mono-volume rendering is data intermixing. In this paper, we present three levels of data intermixing and their rendering pipelines in direct multi-volume rendering, which discriminate image level intensity intermixing, accumulation level opacity intermixing, and illumination model level parameter intermixing. In the context of radiotherapy treatment planning, different data intermixing methods are applied to three volumes, including CT volume, Dose volume, and Segmentation volume, to compare the features of different data intermixing methods