Transfer of a quantum state from a photonic qubit to a gate-defined quantum dot

Interconnecting well-functioning, scalable stationary qubits and photonic qubits could substantially advance quantum communication applications and serve to link future quantum processors. Here, we present two protocols for transferring the state of a photonic qubit to a single-spin and to a two-spin qubit hosted in gate-defined quantum dots (GDQD). Both protocols are based on using a localized exciton as intermediary between the photonic and the spin qubit. We use effective Hamiltonian models to describe the hybrid systems formed by the the exciton and the GDQDs and apply simple but realistic noise models to analyze the viability of the proposed protocols. Using realistic parameters, we find that the protocols can be completed with a success probability ranging between 85-97%

Datengetriebene Verfahren für Tempo- und Tonart-Schätzung von Musikaufnahmen

In recent years, we have witnessed the creation of large digital music collections, accessible, for example, via streaming services. Efficient retrieval from such collections, which goes beyond simple text searches, requires automated music analysis methods. Creating such methods is a central part of the research area Music Information Retrieval (MIR). In this thesis, we propose, explore, and analyze novel data-driven approaches for the two MIR analysis tasks tempo and key estimation for music recordings. Tempo estimation is often defined as determining the number of times a person would “tap” per time interval when listening to music. Key estimation labels music recordings with a chord name describing its tonal center, e.g., C major. Both tasks are well established in MIR research. To improve tempo estimation, we focus mainly on shortcomings of existing approaches, particularly estimates on the wrong metrical level, known as octave errors. We first propose novel methods using digital signal processing and traditional feature engineering. We then re-formulate the signal-processing pipeline as a deep computational graph with trainable weights. This allows us to take a purely data-driven approach using supervised machine learning (ML) with convolutional neural networks (CNN). We find that the same kinds of networks can also be used for key estimation by changing the orientation of directional filters. To improve our understanding of these systems, we systematically explore network architectures for both global and local estimation, with varying depths and filter shapes, as well as different ways of splitting datasets for training, validation, and testing. In particular, we investigate the effects of learning on different splits of cross-version datasets, i.e., datasets that contain multiple recordings of the same pieces. For training and evaluation the proposed data-driven approaches rely on curated datasets covering certain key and tempo ranges as well as genres. Datasets are therefore another focus of this work. Additionally to creating or deriving new datasets for both tasks, we evaluate the quality and suitability of popular tempo datasets and metrics, and conclude that there is ample room for improvement. To promote better, transparent evaluation, we propose new metrics and establish a large open and public repository containing evaluation code, reference annotations, and estimates.In den vergangenen Jahren sind große digitale Musiksammlungen entstanden, die – beispielsweise – über Streaming-Dienste einfach zugänglich sind. Ein effizientes Retrieval aus solchen Sammlungen, das über die simple Textsuche hinausgeht, erfordert automatisierte Musikanalysemethoden. Das Erforschen solcher Methoden ist ein zentraler Bestandteil des Forschungsgebiets Music Information Retrieval (MIR). In dieser Arbeit stellen wir neue datengetriebene Ansätze für die beiden MIR-Analyseaufgaben Tempo- und Tonart-Schätzung für Musikaufnahmen vor und analysieren sie. Dabei wird Tempo-Schätzung oft definiert als das Zählen der Male, die eine Person beim Hören von Musik pro Zeitintervall “klopfen” würde. Tonart-Schätzung weist Musikaufnahmen einen Akkordnamen zu, der den Klangmittelpunkt beschreibt, z.B. C-Dur. Beide Aufgaben sind in der MIR-Forschung fest verankert. Um die Tempo-Schätzung zu verbessern, konzentrieren wir uns hauptsächlich auf Defizite bestehender Ansätze, insbesondere Schätzungen auf der falschen metrischen Ebene, den sogenannten Oktavfehlern. Dazu schlagen wir zunächst neue Methoden vor, die sich der digitalen Signalverarbeitung und des traditionellen Feature-Engineerings bedienen. Anschließend formulieren wir die Signalverarbeitungspipeline in eine tiefe, graphenartige Rechenstruktur mit trainierbaren Parametern um. Dies ermöglicht uns einen rein datengetriebenen Ansatz unter Verwendung von überwachtem maschinellem Lernen (ML) mit neuronalen Netzen – insbesondere Convolutional Neural Networks (CNN). Wir stellen fest, dass durch das ändern der Orientierung von gerichteten Filtern, die gleichen Arten von Netzwerken auch für die Tonart-Schätzung verwendet werden können. Um unser Verständnis dieser Systeme zu vertiefen, untersuchen wir systematisch Netzwerkarchitekturen für die globale und lokale Schätzung mit unterschiedlichen Tiefen und Filterformen sowie verschiedenen Datensatz-Splits für Training, Validierung und Test. Insbesondere betrachten wir, welche Auswirkungen das Lernen auf verschiedenen Splits von Cross-Version-Datensätzen hat. Dies sind Datensätze, die mehrere Aufnahmen derselben Stücke enthalten. Für Training und Evaluation stützen sich die vorgeschlagenen datengetriebenen Ansätze auf kuratierte Datensätze, die bestimmte Tonart- und Tempobereiche sowie Genres abdecken. Ein weiterer Schwerpunkt dieser Arbeit liegt daher auf den Datensätzen selbst. Zusätzlich zum Erstellen oder Ableiten neuer Datensätze für beide o.g. Aufgaben evaluieren wir die Qualität und Eignung gängiger Tempo-Datensätze und -Metriken und kommen zu dem Schluss, dass es Raum für Verbesserungen gibt. Um eine bessere, transparentere Evaluation zu fördern, schlagen wir daher neue Metriken vor und etablieren ein großes, offenes und öffentliches Repository mit Evaluationscode, Referenzannotationen und Schätzungen

Temperature elevations can induce switches to homoclinic action potentials that alter neural encoding and synchronization

The article processing charge was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491192747 and the Open Access Publication Fund of Humboldt-Universität zu Berlin.Almost seventy years after the discovery of the mechanisms of action potential generation, some aspects of their computational consequences are still not fully understood. Based on mathematical modeling, we here explore a type of action potential dynamics – arising from a saddle-node homoclinic orbit bifurcation - that so far has received little attention. We show that this type of dynamics is to be expected by specific changes in common physiological parameters, like an elevation of temperature. Moreover, we demonstrate that it favours synchronization patterns in networks – a feature that becomes particularly prominent when system parameters change such that homoclinic spiking is induced. Supported by in-vitro hallmarks for homoclinic spikes in the rodent brain, we hypothesize that the prevalence of homoclinic spikes in the brain may be underestimated and provide a missing link between the impact of biophysical parameters on abrupt transitions between asynchronous and synchronous states of electrical activity in the brain.Peer Reviewe

Calculation of tunnel-couplings in open gate-defined disordered quantum dot systems

Quantum computation based on semiconductor electron-spin qubits requires high control of tunnel-couplings, both across quantum dots and between the quantum dot and the reservoir. The tunnel-coupling to the reservoir sets the qubit detection and initialization bandwidth for energy-resolved spin-to-charge conversion and is essential to tune single-electron transistors commonly used as charge detectors. Potential disorder and the increasing complexity of the two-dimensional gate-defined quantum computing devices sets high demands on the gate design and the voltage tuning of the tunnel barriers. We present a Green's formalism approach for the calculation of tunnel-couplings between a quantum dot and a reservoir. Our method takes into account in full detail the two-dimensional electrostatic potential of the quantum dot, the tunnel barrier and reservoir. A Markov approximation is only employed far away from the tunnel barrier region where the density of states is sufficiently large. We calculate the tunnel-coupling including potential disorder effects, which become increasingly important for large-scale silicon-based spin-qubit devices. Studying the tunnel-couplings of a single-electron transistor in Si/SiGe as a showcase, we find that charged defects are the dominant source of disorder leading to variations in the tunnel-coupling of four orders of magnitude.Comment: 10 pages, 4 figure

A dynamic clamp protocol to artificially modify cell capacitance

Dynamics of excitable cells and networks depend on the membrane time constant, set by membrane resistance and capacitance. Whereas pharmacological and genetic manipulations of ionic conductances of excitable membranes are routine in electrophysiology, experimental control over capacitance remains a challenge. Here, we present capacitance clamp, an approach that allows electrophysiologists to mimic a modified capacitance in biological neurons via an unconventional application of the dynamic clamp technique. We first demonstrate the feasibility to quantitatively modulate capacitance in a mathematical neuron model and then confirm the functionality of capacitance clamp in in vitro experiments in granule cells of rodent dentate gyrus with up to threefold virtual capacitance changes. Clamping of capacitance thus constitutes a novel technique to probe and decipher mechanisms of neuronal signaling in ways that were so far inaccessible to experimental electrophysiology.Peer Reviewe

High-resolution nerve ultrasound abnormalities in POEMS syndrome: a comparative study

Background: High-resolution nerve ultrasound (HRUS) has been proven to be a valuable tool in the diagnosis of immune-mediated neuropathies, such as chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, M-protein, skin changes) is an important differential diagnosis of CIDP. Until now, there have been no studies that could identify specific HRUS abnormalities in POEMS syndrome patients. Thus, the aim of this study was to assess possible changes and compare findings with CIDP patients. Methods: We retrospectively analyzed HRUS findings in three POEMS syndrome and ten CIDP patients by evaluating cross-sectional nerve area (CSA), echogenicity and additionally calculating ultrasound pattern scores (UPSA, UPSB, UPSC and UPSS) and homogeneity scores (HS). Results: CIDP patients showed greater CSA enlargement and higher UPSS (median 14 vs. 11), UPSA (median 11.5 vs. 8) and HS (median 5 vs. 3) compared with POEMS syndrome patients. However, every POEMS syndrome patient illustrated enlarged nerves exceeding reference values, which were not restricted to entrapment sites. In CIDP and POEMS syndrome, heterogeneous enlargement patterns could be identified, such as inhomogeneous, homogeneous and regional nerve enlargement. HRUS in CIDP patients visualized both increased and decreased echointensity, while POEMS syndrome patients pictured hypoechoic nerves with hyperechoic intraneural connective tissue. Discussion: This is the first study to demonstrate HRUS abnormalities in POEMS syndrome outside of common entrapment sites. Although nerve enlargement was more prominent in CIDP, POEMS syndrome patients revealed distinct echogenicity patterns, which might aid in its differentiation from CIDP. Future studies should consider HRUS and its possible role in determining diagnosis, prognosis and treatment response in POEMS syndrome

TYK2 inhibition and its potential in the treatment of chronic inflammatory immune diseases

Immune-mediated chronic inflammatory diseases have emerged as a leading cause of morbidity and mortality in Western countries over the last decades. Although multiple putative factors have been suspected to be causally related to the diseases, their overarching etiology remains unknown. This review article summarizes the current state of scientific knowledge and understanding of the role of non-receptor tyrosine kinases, with a special focus on the Janus kinase TYK2 in autoimmune and immune mediated diseases as well as on the clinical properties of its inhibition. A panel of experts in the field discussed the scientific evidence and molecular rationale for TYK2 inhibition and its clinical application. Reviewing this meeting, we aim at providing an integrated overview of the clinical profile of TYK2 inhibition and its potential in targeted pharmacological therapy of chronic autoimmune and immune-mediated diseases, with a special focus on inflammatory diseases of the skin