Abstract: Over the past decade, machine learning techniques have revolutionized how scientific research is done, from designing new materials to finding significant events in particle physics to assisting drug discovery. Recently, we added to this list by showing how a machine learning algorithm, combined with optimization routines, can assist experimental efforts in tuning semiconductor quantum dot devices. In particular, we demonstrated that deep convolutional neural networks can be used to characterize the state and charge configuration of single and double quantum dots devices based on measurements of a current-gate voltage transport characteristics or via the conductance of a nearby charge sensor [1]. Our approach provides a paradigm for fully-automated experimental initialization through a closed-loop system that does not rely on human intuition and experience.
Here I expand upon our prior work to show how a machine learning-based approach can be applied for pattern recognition to higher-dimensional systems. Given the recent progress in the physical construction of systems with N >> 3 gates to create a large number of dots, in both one and two dimensions [2,3], it is imperative to have a reliable method to find a stable, desirable electron configuration in the dot array. I will present a preliminary approach that differs from the conventional machine learning literature, in which we consider the benefit of using a “fingerprint” of state space. Rather than working with full-sized sweeps of the gate voltage space, we train a machine-learning algorithm using use 1D traces (“rays”) of fixed length in multiple directions to recognize relative position of the features characterizing given state (i.e., to “fingerprint”) in order to differentiate between various state configurations. We use a double dot device as a toy model to compare with our existing, CNN approach, and then show how this fingerprinting can extend to higher-dimensional systems. Our approach not only allows to automate the recognition of states, but also to reduce the number of measurements required for tuning.
[1] S.S. Kalantre, J.P. Zwolak, S. Ragole, X. Wu, N. M. Zimmerman, M.D. Stewart, Jr., J.M. Taylor, arXiv:1712.04914 (2017).
[2] D.M. Zajac, T.M. Hazard, X. Mi, E. Nielsen, J.R. Petta, Phys. Rev. Appl. 6, 054013 (2016).
[3] U. Mukhopadhyay, J. P. Dehollain, C. Reichl, W. Wegscheider, L. M. K. Vandersypen, Appl. Phys. Lett. 112, 183505 (2018).