Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Auto-tuning of double dot devices in situ with machine learning   The current practice of manually tuning quantum dots (QDs) for qubit
operation is a relatively time-consuming procedure that is inherently
impractical for scaling up and applications. In this work, we report on the
{\it in situ} implementation of a recently proposed autotuning protocol that
combines machine learning (ML) with an optimization routine to navigate the
parameter space. In particular, we show that a ML algorithm trained using
exclusively simulated data to quantitatively classify the state of a double-QD
device can be used to replace human heuristics in the tuning of gate voltages
in real devices. We demonstrate active feedback of a functional double-dot
device operated at millikelvin temperatures and discuss success rates as a
function of the initial conditions and the device performance. Modifications to
the training network, fitness function, and optimizer are discussed as a path
toward further improvement in the success rate when starting both near and far
detuned from the target double-dot range.

36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph