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Characterisation and fully autonomous tuning of spin qubits with machine learning

Jonas Schuff

Published Jan 1, 2024• 0 citations
Publisher

Abstract

Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real ‘needle in the haystack’ problem, which, until now, has resisted complete automation due to device variability, fabrication imperfections, and limited data availability. In this thesis, I first present a machine learning algorithm capable of automatically identifying Pauli spin blockade (PSB) using charge transport measurements. PSB can be employed as a resource for spin qubit initialisation and readout but it can be difficult to identify. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. I demonstrate the approach on a silicon field-effect transistor device and report a high accuracy on test devices, giving evidence that the approach is robust to device variability. Then, I present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. This automation, achieved without human intervention, is demonstrated in a Ge/Si core/shell nanowire device and integrates deep learning, Bayesian optimisation, and computer vision techniques. Further, I demonstrate the potential of full automation by characterising how the Rabi frequency and g-factor depend on barrier gate voltages for qubits in four different charge transitions. The data reveals that certain features in charge transport measurements can enhance automated tuning processes by recognising the conditions for successful readout. Finally, I present the autonomous optimisation of two qubits for maximal Rabi frequency. This optimisation extends the tuning algorithm, enabling it to enhance the performance of a qubit. I hope the mass tuning and characterisation of qubits, enabled by the methods in this thesis, will create a productive feedback loop between measurement and fabrication processes. Wafer-scale, high-throughput characterisation of quantum devices can mitigate device variability and contribute to their scalability. The continued evolution of these methods will support the growing demands of the field, ultimately – and hopefully – leading to practical and powerful quantum computers.

DOI
10.5287/ora-x5pzq8njm

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Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real ‘needle in the haystack’ problem, which, until now, has resisted complete automation due to device variability, fabrication imperfections, and limited data availability. In this thesis, I first present a machine learning algorithm capable of automatically identifying Pauli spin blockade (PSB) using charge transport measurements. PSB can be employed as a resource for spin qubit initialisation and readout but it can be difficult to identify. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. I demonstrate the approach on a silicon field-effect transistor device and report a high accuracy on test devices, giving evidence that the approach is robust to device variability. Then, I present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. This automation, achieved without human intervention, is demonstrated in a Ge/Si core/shell nanowire device and integrates deep learning, Bayesian optimisation, and computer vision techniques. Further, I demonstrate the potential of full automation by characterising how the Rabi frequency and g-factor depend on barrier gate voltages for qubits in four different charge transitions. The data reveals that certain features in charge transport measurements can enhance automated tuning processes by recognising the conditions for successful readout. Finally, I present the autonomous optimisation of two qubits for maximal Rabi frequency. This optimisation extends the tuning algorithm, enabling it to enhance the performance of a qubit. I hope the mass tuning and characterisation of qubits, enabled by the methods in this thesis, will create a productive feedback loop between measurement and fabrication processes. Wafer-scale, high-throughput characterisation of quantum devices can mitigate device variability and contribute to their scalability. The continued evolution of these methods will support the growing demands of the field, ultimately – and hopefully – leading to practical and powerful quantum computers.
Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. Our algorithm, an essential step for realising fully automatic qubit tuning, is expected to be employable across all types of quantum dot devices.
Semiconducting spin quantum devices present a promising avenue for advancing noisy intermediate-scale quantum (NISQ) computers. However, the complexities involved in their tuning and characterisation currently hinder their scaling. This thesis addresses these challenges by harnessing machine-learning-based approaches. Firstly, I used Gaussian processes to develop a fully automatic tuning algorithm that exclusively used radio-frequency measurements taken on millisecond timescales. Secondly, I employed Bayesian optimisation for charge sensor optimisation and automation. Both algorithms were deployed on depletion mode hole-based Ge/SiGe devices; however, they could easily be applied to other architectures. These automated approaches considerably outperformed manual tuning and represent vital building blocks for algorithms to tune larger quantum dot arrays. Next, I developed algorithms to find the ground state charge configuration in the constant capacitance model, which was orders of magnitude faster than its predecessors, permitting the simulation of much larger arrays of sixteen dots. This open-source package can be used to gain insights into the charge stability diagrams of large quantum dot arrays and facilitates further tuning algorithm development. Lastly, I present a novel application of hidden Markov models to identify and separate the state preparation, measurement and spin-flip errors presented by a pair of hot Loss Divincenzo qubits in a Si-MOS device. This approach not only unravels these error sources but also sheds light on their relationship with various parameters, such as electron temperature, thus contributing a vital understanding to the field of quantum computing.
Donor spin qubits in silicon offer one- and two-qubit gates with fidelities beyond 99%, coherence times exceeding 30 seconds, and compatibility with industrial manufacturing methods. This motivates the development of large-scale quantum processors using this platform, and the ability to automatically tune and operate such complex devices. In this work, we present the first machine learning algorithm with the ability to automatically locate the charge transitions of an ion-implanted donor in a silicon device, tune single-shot charge readout, and identify the gate voltage parameters where tunnelling rates in and out the donor site are the same. The entire tuning pipeline is completed on the order of minutes. Our results enable both automatic characterisation and tuning of a donor in silicon devices faster than human experts.
Abstract Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. The approach is expected to be employable across all types of quantum dot devices.
Efficient tuning of spin qubits remains a major bottleneck in scaling semiconductor quantum dot-based quantum processors. A key challenge is the rapid identification of gate voltage regimes suitable for qubit initialisation, control, and readout. Here, we leverage radio-frequency charge sensing to automate spin qubit tuning, achieving a median tuning time of approximately 15 minutes. In a single continuous run, our routine identifies spin qubits at 12 distinct charge transitions in under 17 hours. Beyond tuning, our routine autonomously acquires data revealing the gate-voltage dependence of the exchange interaction, dephasing time, and quality factor -- quantities that vary substantially between charge configurations. These results represent a step change in high-throughput spin qubit tuning and provide a foundation for a systematic and automated exploration of semiconductor quantum circuits.
Abstract Recent progress in building large-scale quantum devices for exploring quantum computing and simulation has relied upon effective tools for achieving and maintaining good experimental parameters, i.e., tuning up devices. In many cases, including quantum dot-based architectures, the parameter space grows substantially with the number of qubits, and may become a limit to scalability. Fortunately, machine learning techniques for pattern recognition and image classification, using so-called deep neural networks, have shown surprising successes for computer-aided understanding of complex systems. We propose a new paradigm for fully automated experimental initialization through a closed-loop system relying on machine learning and optimization techniques. We use deep convolutional neural networks to characterize states and charge configurations of semiconductor quantum dot arrays when only measurements of a current−voltage characteristic of transport are available. For simplicity, we model a semiconductor nanowire connected to leads and capacitively coupled to depletion gates using the Thomas−Fermi approximation and Coulomb blockade physics. We then generate labeled training data for the neural networks, and find at least 90 % accuracy for charge and state identification for single and double dots. Using these characterization networks, we can then optimize the parameter space to achieve a desired configuration of the array, a technique we call “auto-tuning”. Finally, we show how such techniques can be implemented in an experimental setting by applying our approach to an experimental dataset, and outline further problems in this domain, from using charge sensing data to extensions to full one- and two-dimensional arrays, that can be tackled with machine learning.