



@article {2687,
	title = {Mechanical Quantum Sensing in the Search for Dark Matter},
	year = {2020},
	month = {8/13/2020},
	type = {FERMILAB-PUB-20-378-QIS-T},
	abstract = {<p>Numerous astrophysical and cosmological observations are best explained by the existence of dark matter, a mass density which interacts only very weakly with visible, baryonic matter. Searching for the extremely weak signals produced by this dark matter strongly motivate the development of new, ultra-sensitive detector technologies. Paradigmatic advances in the control and readout of massive mechanical systems, in both the classical and quantum regimes, have enabled unprecedented levels of sensitivity. In this white paper, we outline recent ideas in the potential use of a range of solid-state mechanical sensing technologies to aid in the search for dark matter in a number of energy scales and with a variety of coupling mechanisms.</p>},
	url = {https://arxiv.org/abs/2008.06074},
	author = {D. Carney and G. Krnjaic and D. C. Moore and C. A. Regal and G. Afek and S. Bhave and B. Brubaker and T. Corbitt and J. Cripe and N. Crisosto and A.Geraci and S. Ghosh and J. G. E. Harris and A. Hook and E. W. Kolb and J. Kunjummen and R. F. Lang and T. Li and T. Lin and Z. Liu and J. Lykken and L. Magrini and J. Manley and N. Matsumoto and A. Monte and F. Monteiro and T. Purdy and C. J. Riedel and R. Singh and S. Singh and K. Sinha and J. M. Taylor and J. Qin and D. J. Wilson and Y. Zhao}
}
@article {2103,
	title = {Machine Learning techniques for state recognition and auto-tuning in quantum dots},
	year = {2017},
	month = {2017/12/13},
	abstract = {<p>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\&rsquo;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 \&lsquo;auto-tuning\&rsquo;. 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.</p>},
	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 J. M. Taylor}
}
