Data needs and challenges for quantum dot devices automation
Justyna P. Zwolak, Jacob M. Taylor, Reed W. Andrews, Jared Benson, Garnett W. Bryant, Donovan L. Buterakos, Anasua Chatterjee, S. Das Sarma, M. A. Eriksson, Eliška Greplová, Michael J. Gullans, Fabian Hader, Tyler J. Kovach, Pranav Mundada, Mick Ramsey, Torbjørn Rasmussen, Brandon Severin, Anthony J. Sigillito, Brennan Undseth, Brian Weber
Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.
Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.
Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, a comprehensive overview of the recent progress in the automation of QD device control is presented, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.
We present efficient methods to reliably characterize and tune gate-defined semiconductor spin qubits. Our methods are developed for double quantum dots in GaAs heterostructures, but they can easily be adapted to other quantum-dot-based qubit systems. These tuning procedures include the characterization of the interdot tunnel coupling, the tunnel coupling to the surrounding leads, and the identification of various fast initialization points for the operation of the qubit. Since semiconductor-based spin qubits are compatible with standard semiconductor process technology and hence promise good prospects of scalability, the challenge of efficiently tuning the dot’s parameters will only grow in the near future, once the multiqubit stage is reached. With the anticipation of being used as the basis for future automated tuning protocols, all measurements presented here are fast-to-execute and easy-to-analyze characterization methods. They result in quantitative measures of the relevant qubit parameters within a couple of seconds and require almost no human interference.
Building a quantum computer able to solve real-world problems is facing several challenges, both in software and hardware.Some of these challenges are the physical implementation of a large number of qubits, as well as qubit quality and control.This thesis addresses one of the outstanding challenges -the automated initialisation of solid-state qubits.These qubit types, which encode information in charge, spin or topologically protected quasiparticle states, are based on electrostatically defined quantum dots.In order to define quantum dots, suitable gate voltage combinations need to be determined and several parameters such as the inter-dot tunnel coupling adjusted to desired values.With growing device complexity and an increasing number of devices required for measurements, a manual approach to tuning is impractical.Considering the success of machine learning in many disciplines over recent years, it is a promising tool whose suitability for qubit tuning is explored here.We first implement a two-stage device characterisation and dot-tuning process by automating well-established, manual tuning procedures, where the experimenter's decisions is replaced by supervised machine learning.A demonstration on several previously unmeasured devices shows that such an approach is sufficient to tune devices autonomously and without pre-measured input.Next, we address charge-state detection based on charge stability diagrams, an essential component of automated tuning.Beside our use of simple binary classifiers trained on experimental data, deep learning models trained on synthetic data have been previously used to perform this task.While the use of synthetic data has the advantage of providing the large training datasets required for deep learning without the need to hand label experimental data, it also trains classifiers on data coming from different distributions than the data encountered during tuning.We evaluate the classification accuracy of a range of machine learning models trained on simulated and experimental data, assessing their ability to generalise to experimental charge stability diagrams.We find that experimental training data as well as realistic quantum dot simulations and noise models iii Suominen, these two dragged me out of the lab on a regular basis and made sure all doubts and misery were drowned in bubbles.Their input on how to navigate the first period of new beginnings and changes was priceless.Beside the usual PhD challenges and as a result of living in two places at one, my adventure also included some overhead paperwork.Overwhelmed by it at times, it was more than reassuring to have a friend like Mario Könz.He has always been keen on sharing some of his abundant knowledge, be it in programming or in how to deal with Swiss authorities.My endeavour would have failed without his readiness to help me out on the Swiss paperwork front.Similarly, Anïs Mairena, whose presence already got me through my Master's in Lausanne, also saved me from a lot of admit headache.I am also immensely grateful for crossing the paths of John Hornibrook and Xanthe Croot.As the kindest and most generous people I have ever met, these two made me feel at home far away at home.I will always remember those 30km bush walks with Xanthe, during which I not only got to know some of the QNL lab rats better, but was also able to recharge on positivity, happiness and sunshine.Back in the lab, it was John who patiently listened to my complains and answered all my dumb questions.From my very first steps in python and QCoDeS to the implementation of my tuning package, I had the privilege to be able to count on William H. P Nielsen and Jens H. Nielsen for support.They not only helped me get through writing drivers for instruments I didn't how to use, but their patience, comprehensive feedback and technical expertise made me the programmer I am today.Despite the rather atypical working arrangement, I was blessed to have someone who could comprehend my situation: Natalie Pearson.I was fortunate not to be the only one packing my bags for Copenhagen early in their PhD.We embarked on the Danish journey together and were able to support each other in a new country.In German one would say "geteiltes Leid ist halbes Leid".Furthermore, I would like to mention those who always had a time for a spontaneous chat, ultimately getting me through those days that didn't seem to end:
Over the past decade, semiconductor spin qubits have progressed from few-qubit demonstrations towards larger-scale devices fabricated in increasingly reproducible academic and industrial processes. This progress marks an inflection point: the central challenge is no longer to demonstrate high-fidelity operation in carefully tuned devices, but to discover, verify, and maintain stable operating conditions reliably across many interdependent controls, varied device geometries, and disparate material platforms. In this Perspective, we frame spin-qubit operation as a modular automation problem. We decompose the workflow into five modules: bootstrapping from minimal prior information, configuration tuning, virtualization of physical gates into effective control axes, qubit-level tuning, and an operation layer with drift-aware maintenance. Using recent demonstrations from our work and the broader community, we argue that scalability will depend on explicit interfaces between modules, standardized intermediate data products, and workflow-level metrics such as throughput, success probability, stability time, recovery time, and robustness. We close by outlining the infrastructure needed to move beyond isolated tuning demonstrations toward sustained operation: qubit-performance-aware feedback, reusable software and benchmark tasks, and tight collaboration among experimental, theoretical, and software efforts.
Abstract Tuning gate-defined quantum dots for qubit operation is an often frustrating and time-consuming activity in spin qubit research due to inherent device variability and the complex interactions between dots. Furthermore, tuning these devices grows more complicated as quantum computers grow to utility-scale. Computer-automated and machine learning techniques are popular approaches to accelerate the tuning process. However, these techniques need to become much faster and more accurate to address the tuning of millions of qubits in utility-scale quantum computing. We examine the literature of computer-automated and machine learning spin qubit tuning algorithms, grouping the algorithms by similar tasks and analyzing them from a computer science perspective to keep necessary background knowledge of quantum computing/physics at a minimum. We look at which approaches seem propitious for scaling up to utility-scale systems and where computer scientists could potentially have the most impact.
Semiconductor quantum dot arrays defined electrostatically in a 2D electron gas provide a scalable platform for quantum information processing and quantum simulations. For the operation of quantum dot arrays, appropriate voltages need to be applied to the gate electrodes that define the quantum dot potential landscape. Tuning the gate voltages has proven to be a time-consuming task, because of initial electrostatic disorder and capacitive cross-talk effects. Here, we report on the automated tuning of the inter-dot tunnel coupling in gate-defined semiconductor double quantum dots. The automation of the tuning of the inter-dot tunnel coupling is the next step forward in scalable and efficient control of larger quantum dot arrays. This work greatly reduces the effort of tuning semiconductor quantum dots for quantum information processing and quantum simulation.