High-fidelity, compact readout of spins in silicon quantum dots

Silicon has become one of the leading platforms for quantum computation, having demonstrated qubits with long coherence times and high fidelity operations. Moreover, the similarities between silicon quantum dots and transistors give hope for mass production of qubits easily integrable with control electronics. However, to fully leverage their scalability potential, the footprint of the additional circuits for control and readout needs to be minimised. Here, we introduce a compact spin-readout method based on spin-dependent tunnelling combined with a dispersive charge sensor: the radio-frequency single-electron box (SEB). Opposite to traditional charge sensors, the SEB only requires a single lead, reducing its footprint. Using this sensing technique, we demonstrate spin readout of a single electron spin in a CMOS device manufactured at the 300mm wafer-scale using industrial processes, in which we measure long single spin relaxation times (up to 9 s). Next, we focus on achieving a high readout fidelity, since it is essential to perform error correction and ultimately sets the fidelity of qubit operations. The readout fidelity is partly set by the ability of the sensor to detect rapid events with high accuracy. We demonstrate that a low-loss high-impedance resonator highly coupled to the SEB, together with a Josephson Parameter Amplifier, are central for optimal performance. With these modifications, we obtain an integration time τm = 100 ns for a signal to noise ratio equal to 1, which facilitates single-shot spin readout, reaching a measurement fidelity FM = 99.54%, above the fault-tolerant threshold, in a readout time Δt = 250 μs. We identify that the readout time is limited by the choice of the spin-to-charge conversion mechanism. In the last part of the thesis, we work towards performing Pauli spin blockade spin readout, which does not have such time limitation

El caso Vox: análisis de la comunicación de la campaña a las elecciones nacionales

El siguiente trabajo pretende realizar un análisis sobre la comunicación durante la campaña electoral a las Elecciones Generales celebradas el 28 de abril de 2019 del partido político Vox y como ésta ha ayudado a cumplir sus objetivos electorales. A pesar de ser un tema en auge la investigación va a descomponer su estrategia comunicativa y su mensaje a partir de documentación actual de forma que se pueda entender el proceso que ha seguido esta opción política hasta convertirse en un partido con representación en todos los estratos de poder. Se van a analizar tácticas comunicativas y la publicidad política de Vox en los medios y el contenido de su mensaje demostrando como una campaña política bien planteada es capaz de conseguir el voto de una parte importante de la sociedad.Departamento de Historia Moderna, Contemporánea y de América, Periodismo y Comunicación Audiovisual y PublicidadGrado en Publicidad y Relaciones Pública

Kekulescope: Prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images

The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discovery. However, existing applications often require large data sets for training, or sophisticated pretraining schemes. Here, we show using 33 IC50 data sets from ChEMBL 23 that the in vitro activity of compounds on cancer cell lines and protein targets can be accurately predicted on a continuous scale from their Kekule structure representations alone by extending existing architectures, which were pretrained on unrelated image data sets. We show that the predictive power of the generated models is comparable to that of Random Forest (RF) models and fully-connected Deep Neural Networks trained on circular (Morgan) fingerprints. Notably, including additional fully-connected layers further increases the predictive power of the ConvNets by up to 10%. Analysis of the predictions generated by RF models and ConvNets shows that by simply averaging the output of the RF models and ConvNets we obtain significantly lower errors in prediction for multiple data sets, although the effect size is small, than those obtained with either model alone, indicating that the features extracted by the convolutional layers of the ConvNets provide complementary predictive signal to Morgan fingerprints. Lastly, we show that multi-task ConvNets trained on compound images permit to model COX isoform selectivity on a continuous scale with errors in prediction comparable to the uncertainty of the data. Overall, in this work we present a set of ConvNet architectures for the prediction of compound activity from their Kekule structure representations with state-of-the-art performance, that require no generation of compound descriptors or use of sophisticated image processing techniques

Cabacas Liceranzu sendia eta semearen ustekabeko heriotza. Amaitu gabeko oroimen historia bat.

Iñigo Cabacas Lizeranzu gaztearen ustekabeko heriotz traumatikoa aintzat harturik, haren sendiaren oroimenaren historia bat idatzi dut. Hartara, lanaren lehen atala memoria eta historia (norbera zein den, ze ezaugarri eta xede dituen,...) arteko hartu-emanari eskaini ostean, aipatu kasu azterketari heldu diot. Bigarren zatian, beraz, gurasoen oroimenaren historia osatzen ahalegindu naiz, beti ere Euskal Erkidegoko administrazioaren jokabidearekin uztartuta. Gurasoen oroitzapenaren bilakaera dugu aztergai, beraz, lanaren atal honetan. Hau da, denbora- une ezberdinei so egin diegu, eta ondoren, memoriaren historia osatzen joan eta oroimenaren ezaugarriak (iraganeko gertaera epaitzen ote duen, aldatzen ote den...) betetzen ote diren aztert

Diseño, implementación y análisis del aumento de productividad de un sistema solar en flotación

Diseño, implementación y análisis del aumento de productividad de un sistema solar en flotaciónMáster en Ingeniería de la Bioenergía y Sostenibilidad Energétic