@article {2103, title = {Machine Learning techniques for state recognition and auto-tuning in quantum dots}, year = {2017}, month = {2017/12/13}, abstract = {

Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many cases, including in quantum-dot based architectures, the parameter space grows substantially with the number of qubits, and may become a limit to scalability. Fortunately, machine learning techniques for pattern recognition and image classification using so-called deep neural networks have shown surprising successes for computer-aided understanding of complex systems. In this work, we use deep and convolutional neural networks to characterize states and charge configurations of semiconductor quantum dot arrays when one can only measure a current-voltage characteristic of transport (here conductance) through such a device. For simplicity, we model a semiconductor nanowire connected to leads and capacitively coupled to depletion gates using the Thomas-Fermi approximation and Coulomb blockade physics. We then generate labeled training data for the neural networks, and find at least 90 \% accuracy for charge and state identification for single and double dots purely from the dependence of the nanowire\’s conductance upon gate voltages. Using these characterization networks, we can then optimize the parameter space to achieve a desired configuration of the array, a technique we call \‘auto-tuning\’. Finally, we show how such techniques can be implemented in an experimental setting by applying our approach to an experimental data set, and outline further problems in this domain, from using charge sensing data to extensions to full one and two-dimensional arrays, that can be tackled with machine learning.

}, url = {https://arxiv.org/abs/1712.04914}, author = {Sandesh S. Kalantre and Justyna P. Zwolak and Stephen Ragole and Xingyao Wu and Neil M. Zimmerman and M. D. Stewart and Jacob M. Taylor} } @article {1816, title = {Valley Blockade in a Silicon Double Quantum Dot}, journal = {Physical Review B}, volume = {96}, year = {2017}, month = {2017/11/13}, pages = {205302}, abstract = {

Electrical transport in double quantum dots (DQDs) illuminates many interesting features of the dots\&$\#$39; carrier states. Recent advances in silicon quantum information technologies have renewed interest in the valley states of electrons confined in silicon. Here we show measurements of DC transport through a mesa-etched silicon double quantum dot. Comparing bias triangles (i.e., regions of allowed current in DQDs) at positive and negative bias voltages we find a systematic asymmetry in the size of the bias triangles at the two bias polarities. Asymmetries of this nature are associated with blocking of tunneling events due to the occupation of a metastable state. Several features of our data lead us to conclude that the states involved are not simple spin states. Rather, we develop a model based on selective filling of valley states in the DQD that is consistent with all of the qualitative features of our data.

}, doi = {10.1103/PhysRevB.96.205302}, url = {https://arxiv.org/abs/1607.06107}, author = {Justin K. Perron and Michael Gullans and Jacob M. Taylor and M. D. Stewart, Jr. and Neil M. Zimmerman} }