Identifying Pauli spin blockade using deep learning

Jonas Schuff1, Dominic T. Lennon1, Simon Geyer2, David L. Craig1, Federico Fedele1, Florian Vigneau1, Leon C. Camenzind2, Andreas V. Kuhlmann2, G. Andrew D. Briggs1, Dominik M. Zumbühl2, Dino Sejdinovic3, and Natalia Ares4

1Department of Materials, University of Oxford, Oxford OX1 3PH, United Kingdom
2Department of Physics, University of Basel, 4056 Basel, Switzerland
3School of Computer and Mathematical Sciences & AIML, University of Adelaide, SA 5005, Australia
4Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom

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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.

We developed a machine learning algorithm to automatically detect an elusive effect related to the operation of devices that currently appear among the preferred candidate architectures for quantum technologies, semiconductor qubits. This is an important step towards scalable quantum computation with semiconductor circuits. The effect, Pauli spin blockade (PSB), can be used for initiating and reading out qubits, a fundamental requirement of quantum computing. However, detecting PSB is challenging due to its rarity and sensitivity to material variances and fabrication defects. To overcome this, we used a physics-inspired simulator and a method called cross-device validation, training the algorithm on data from one device and testing it on another. Demonstrated on a silicon field-effect transistor device, the algorithm achieved 96% accuracy in identifying PSB across different test devices. Interestingly, the study found simulated data to be more event important for training the algorithm than real-world data, mainly due to the limited availability of comprehensive experimental data. This research accelerates the realization of practical, scalable quantum computers.

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[1] Ludmila Szulakowska and Jun Dai, "Bayesian autotuning of Hubbard model quantum simulators", arXiv:2210.03077, (2022).

[2] Anuranan Das, Adil Khan, Ankan Mukherjee, and Bhaskaran Muralidharan, "Machine learning unveils multiple Pauli blockades in the transport spectroscopy of bilayer graphene double-quantum dots", arXiv:2308.04937, (2023).

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