Peer review of: Machine learning enables completely automatic tuning of a quantum device faster than human ex (2020)

Reviewers' comments: 
Reviewer #1 (Remarks to the Author): 

The manuscript by Moon et al. presents experimental tests of an autotuning algorithm that takes a 
GaAs-based gate-defined QD device with 8 gate electrodes and tunes the device into a double 
quantum dot regime in a little over an hour. The same success was achieved in another DQD device, 
and the authors investigated different aspects of their tuning algorithm and evaluates their relative 
contribution to the tuning success. 

This is nice and detailed experimental work with immediate relevance to the QD community. Over the 
recent years, gate-defined quantum dots have been gradually scaling up beyond single-qubit regimes 
and the complexity of device tuning correspondingly grows at a very fast rate. Efforts to automate the 
dot tuning process has naturally become a technical goal of the community, with quite a few papers 
from several groups in this area. On the hand, due to the presence of disorder in QD devices, the 
large device-to-device variability has precluded fully automated tuning of DQD from "scratch" (i.e. 
human intervention is usually needed to tune the device into a vicinity of a working regime before the 
computer takes over). This is, to my knowledge, one of the first works that shows fully automated 
tuning from a blank slate. Although there are limitations to the authors' work (the stability diagram 
out of the automated tuning seems rather suboptimal to my eye), the full automation is an important 
feature of the authors' work that warrants a publication in Nat. Comm. 

It would be nice to address a couple of questions before publication: 

1. Why haven't the authors utilized the QPC that seems to be present on their device? Transport 
measurements are largely considered to be very coarse and have limited functionality beyond getting 
the device roughly where it should be. Charge sensor measurements are generally much cleaner. 
Maybe the authors should clarify why they have chosen transport measurements, and evaluate 
possible extension of their algorithm to QPC detection. 

2. Is the stability diagram in Fig. 2 achieved using auto-tuning or human tuning? Maybe I missed it, 
but it's important to clarify if it is achieved by human tuning as it is significantly nicer than the plots 
from machine tuning. 




Reviewer #2 (Remarks to the Author): 

The paper describes a method for tuning quantum dot devices in an up to 
eight-dimensional gate voltage space in under 70 minutes, exceeding a human 
benchmark. 

The proposed approach is claimed to be readily applicable to different material 
systems and device architectures. 

The proposed method can give a quantitative measurement of device variability. 

The work includes an ablation study to identify the benefits of each of its individual components. 

The proposed method includes two key phases. The first one is the sampling phase, where the 
algorithm aims at identifying where transport features are likely to be found on a hypersurface that 
separates low and high current regions in parameter space. 

Overall, I found the paper hard to read and too confusing in some parts. I encourage the authors to 
revise the document to improve its clarity as much as possible. 

I would explain better what a pinched-off region is and why it originates. 

The pruning operation is hard to understand. Its motivation and clarity should be improved. 

What does it mean "in the vicinity of a given v"? 

I found the plots in Figure 3 hard to understand, especially part b). What is the green circle in this plot? 

The motivation for the peak detection steps should be more clear. The same for the Score function 
decision step. 

Why are V_3 and V_7 chosen for the peak detection step? 

What happens if the high-resolution current map is checked by a human and the human reports that 
the device is not tuned to the double quantum dot regime? 

Is this step, where a human has to check the output of the algorithm strictly necessary? If so, the 
proposed approach is not fully automated, which is undesirable. 

I am concerned about the lack of reproducibility of the work due to the high complexity of the 
proposed algorithm, the many different parts and the lack of full details. 

Will the authors provide any source code implementing their algorithm for reproducibility? 

I believe the authors should address the previous comments before the paper can be accepted for 
publication. 




Reviewer #3 (Remarks to the Author): 

This paper presents a machine learning (ML) algorithm that is able to automatically tune quantum dot 
devices made from electrostatically gated GaAs heterostructures. The algorithm uses a module 
(‘Sampling Phase’) that determines a ‘hypersurface’ in the space of 8 gate voltages (V1, V2 ... V8) 
that define the quantum dot device. The hypersurface is the region in gate-voltage space where the 
threshold for current flow occurs. It is in this vicinity where single electron tunnelling through the 
quantum dots occurs. A second module in the algorithm (‘Investigation Phase’) then searches for 
Coulomb blockade (CB) oscillations, which occur near the transport threshold, in the vicinity of the 
hypersurface. Regular CB oscillations are a clear signature that a well-defined quantum dot has been 
formed. 

The paper demonstrates that the designed algorithm performs much better than a ‘random’ selection 
of gate voltages, followed by the search for oscillations, which is what one would hope and expect, 
showing that this algorithm is working efficiently. 

The paper also demonstrates that the algorithm can find ‘double quantum dot’ transport features in an 
average time of 2.8 hrs for Device 1 and 1.1 hrs for Device 2. These times are achieved without any a 
priori knowledge of the ‘function’ or purpose of the gate electrodes. When one uses the ‘design 
knowledge’ that certain gates perform similar functions, the algorithm can then perform device tuning 
into the double-dot regime in 0.6 hrs. 

The authors make the point (based on a private communication with Ferdinand Kuemmeth, and others) 
that the typical time for humans to tune up such devices is around 3 hours, thus justifying the main 
claim of their paper that this machine learning algorithm can outperform ‘human experts’. 
In my opinion the algorithm presented here is novel, although I note that there have been a number 
of recent papers all exploring different approaches to automatically tune quantum dot devices, some 
of which also involve machine learning. Many of these papers are indeed referenced in this current 
submission. 

I also believe that the analysis of the algorithm’s performance is sound and has been done carefully. 
The methodology is also well explained in the text, and the appendices. 

Despite this, I am not convinced that this work reaches the level of significance and impact needed to 
justify publication in Nature Communications. One reason for my judgment is that the quality of the 
double quantum dot stability maps obtained (in Figs. 4 & 5) is generally quite poor. This could simply 
be because there was considerable disorder in the devices themselves, but I find this surprising, since 
these were GaAs quantum dots, which are generally very low disorder and now routinely show high-
quality multiple quantum dot behavior. 

Clear double-dot stability maps for GaAs devices are now routinely demonstrated by many groups 
worldwide. For example, if one refers to the stability maps produced by the auto-tuning algorithms 
presented in Ref. [12] – Mills et al., APL 115, 113501 (2019), or Ref. [13] – Zwolak et al., arXiv: 
1909.08030, these are very clearly due to well-defined double quantum dots. In contrast, while some 
of the features in Figs. 4 & 5 of the submitted manuscript do appear to be related to double-dot-like 
potentials, it is not at all clear whether these are just due to random disorder in the samples, or if 
they are the intended dots as defined by the gate electrodes. 

I think this paper would have more impact in the field if the stability maps generated by the ML 
algorithm were more convincingly those intended by the device design. One suggestion is that the 
authors try some other devices which show clear double-dot stability maps (perhaps when generated 
by human control), and then try out the algorithm on those. 

The paper as it stands, however, is clearly publishable, but I would suggest it was more suitable to a 
more specialized journal, perhaps IoP’s Quantum Science & Technology?