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


RR2

 REVIEWERS' COMMENTS:
Reviewer #1 (Remarks to the Author):
The authors have satisfactorily addressed my comments and I recommend publication.
Reviewer #2 (Remarks to the Author):
Reviewer #3 (Remarks to the Author):
    I have looked at the updated version of the paper and I think the authors have successfully addressed
 my concerns. I believe the new detailed description of the proposed algorithm, together with figures 3
 and 4, is very informative.
  In this revised submission the authors have worked hard to further explain the details of the ML
 algorithm and how it works, as requested by Reviewer 2. I found the original version relatively clear,
 and the extra information now provided makes it even clearer. In general, I have no concerns about
 the explanations of the algorithm, or indeed its validity.
 The main concern that I noted in my previous review was that it was not clear to me that the
 algorithm was reliably able to tune the devices into a double-dot bias regime that would be useful for
 undertaking spin qubit operations, which is the motivation for this work. The issue as I saw it was that
 the quality of the double dot stability maps shown in Figs. 4 & 5 is quite poor, and so it was not clear
 to me that these bias conditions were really the ones that a researcher would want to use for qubit
 measurements.
 In their response letter (in response to a question by Reviewer 1), and in the revised manuscript, the
 authors have pointed out that the double-dot stability map data shown in Fig. 1b (& Fig. 2 of revised
 manuscript) were, in fact, obtained using their ML algorithm. I had not realised this earlier, and I
 thank Reviewer 1 for asking this question. This stability does indeed appear to be what one would
 expect for the device structure shown in Figure 1a, and provides confidence that the algorithm is able
 to (on some occasions at least) configure the gate biases into a region where spin qubit experiments
 could be further attempted.
 Furthermore, in their response to my concern, the authors have pointed out the very important fact
 that they have also recently posted another paper (arXiv:2001.04409, Ref. [21]) which describes an
 additional ML algorithm that they have developed, which goes beyond the initial ‘coarse biasing’
 described in the present manuscript, and which enables ‘fine-tuning’ of the biases into a regime which
 is better suited for spin qubit measurements.
 I’ve now taken a look at their related paper (arXiv:2001.04409), and it seems like this second ML
 algorithm, when combined with the one described in the present submission, could work well together,
 and could produce biasing conditions suitable for studying spin effects, such as spin blockade, in
 arbitrarily configured quantum dot devices.
 In an ideal world, it would be nice if the two papers were combined into one coordinated story, so that
 one could see the ‘complete picture’ of going from scratch to a well-tuned double dot. However, I do
 appreciate that this would be a very long paper, and the two algorithms were likely developed by
 different researchers in the Oxford group.

 Another concern that I have with this technique (and I recognise that I didn’t raise this in my first
 review), is that it is designed purely for electrical transport measurements, rather than for charge
 sensing. This is an issue because it is very difficult to demonstrate spin qubits (in quantum dots) with
 only transport-based sensing, which is why most groups studying spin qubits use either a QPC or SET
 charge senor, or gate-based dispersive readout. As a result, I see it as unlikely that the algorithm
 described here, which is based around finding ‘current thresholds’ or ‘pinch off’ points, will gain much
 utility in future spin qubit experiments. I do agree with the authors, though, that it could be useful to
 assess the ‘variability’ of test devices, in order to assess new fabrication processes, for example.
 Overall, I’m still not sure that this paper reaches the level of impact commensurate with Nature
 Communications. Having said that, I do think it is an excellent paper, and many in the quantum
 device measurement community will find it interesting, primarily because it demonstrates an
 automated way to find threshold voltages ‘from scratch’.