Mesoscopic spin confinement during acoustically induced transport

Long coherence lifetimes of electron spins transported using moving potential dots are shown to result from the mesoscopic confinement of the spin vector. The confinement dimensions required for spin control are governed by the characteristic spin-orbit length of the electron spins, which must be larger than the dimensions of the dot potential. We show that the coherence lifetime of the electron spins is independent of the local carrier densities within each potential dot and that the precession frequency, which is determined by the Dresselhaus contribution to the spin-orbit coupling, can be modified by varying the sample dimensions resulting in predictable changes in the spin-orbit length and, consequently, in the spin coherence lifetime.Comment: 10 pages, 2 figure

Bridging the reality gap in quantum devices with physics-aware machine learning

The discrepancies between reality and simulation impede the optimization and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap using physics-aware machine learning, in particular, using an approach combining a physical model, deep learning, Gaussian random field, and Bayesian inference. This approach enables us to infer the disorder potential of a nanoscale electronic device from electron-transport data. This inference is validated by verifying the algorithm’s predictions about the gate-voltage values required for a laterally defined quantum-dot device in AlGaAs/GaAs to produce current features corresponding to a double-quantum-dot regime

Out-of-equilibrium singlet-triplet Kondo effect in a single C_60 quantum dot

We have used an electromigration technique to fabricate a $\rm{C_{{60}}}$ single-molecule transistor (SMT). Besides describing our electromigration procedure, we focus and present an experimental study of a single molecule quantum dot containing an even number of electrons, revealing, for two different samples, a clear out-of-equilibrium Kondo effect. Low temperature magneto-transport studies are provided, which demonstrates a Zeeman splitting of the finite bias anomaly.Comment: 6 pages, 4 figure

Quantum device fine-tuning using unsupervised embedding learning

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies

Nonequilibrium Singlet-Triplet Kondo Effect in Carbon Nanotubes

The Kondo-effect is a many-body phenomenon arising due to conduction electrons scattering off a localized spin. Coherent spin-flip scattering off such a quantum impurity correlates the conduction electrons and at low temperature this leads to a zero-bias conductance anomaly. This has become a common signature in bias-spectroscopy of single-electron transistors, observed in GaAs quantum dots as well as in various single-molecule transistors. While the zero-bias Kondo effect is well established it remains uncertain to what extent Kondo correlations persist in non-equilibrium situations where inelastic processes induce decoherence. Here we report on a pronounced conductance peak observed at finite bias-voltage in a carbon nanotube quantum dot in the spin singlet ground state. We explain this finite-bias conductance anomaly by a nonequilibrium Kondo-effect involving excitations into a spin triplet state. Excellent agreement between calculated and measured nonlinear conductance is obtained, thus strongly supporting the correlated nature of this nonequilibrium resonance.Comment: 21 pages, 5 figure

Extending glacier monitoring into the Little Ice Age and beyond

Reconstructions of glacier front variations based on well-dated historical evidence from the Alps, Scandinavia, and the southern Andes, extend the observational record as far back as the 16th century. The standardized compilation of paleo-glacier length changes is now an integral part of the internationally coordinated glacier monitoring system

Evidence from the archives of societies: historical sources in glaciology

Glaciers have been recognized as key indicators of climate change. To assess the current decline in glaciers worldwide, their changes must be compared with natural glacier fluctuations since the end of the last ice age. To reconstruct glacier changes over recent centuries, historical methods have proven especially valuable. Pictorial and cartographical documents as well as written accounts can provide a detailed picture of glacier fluctuations, in particular frontal length changes