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

Find this paper interesting or want to discuss? Scite or leave a comment on SciRate.


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.

► BibTeX data

► References

[1] Daniel Loss and David P DiVincenzo. Quantum computation with quantum dots. Physical Review A, 57 (1): 120, 1998. 10.1103/​PhysRevA.57.120.

[2] LMK Vandersypen, H Bluhm, JS Clarke, AS Dzurak, R Ishihara, A Morello, DJ Reilly, LR Schreiber, and M Veldhorst. Interfacing spin qubits in quantum dots and donors - hot, dense, and coherent. npj Quantum Information, 3 (1): 1–10, 2017. 10.1038/​s41534-017-0038-y.

[3] Toivo Hensgens, Takafumi Fujita, Laurens Janssen, Xiao Li, CJ Van Diepen, Christian Reichl, Werner Wegscheider, S Das Sarma, and Lieven MK Vandersypen. Quantum simulation of a Fermi–Hubbard model using a semiconductor quantum dot array. Nature, 548 (7665): 70–73, 2017. 10.1038/​nature23022.

[4] Menno Veldhorst, CH Yang, JCC Hwang, W Huang, JP Dehollain, JT Muhonen, S Simmons, A Laucht, FE Hudson, Kohei M Itoh, et al. A two-qubit logic gate in silicon. Nature, 526 (7573): 410–414, 2015. 10.1038/​nature15263.

[5] Pascal Cerfontaine, Tim Botzem, Julian Ritzmann, Simon Sebastian Humpohl, Arne Ludwig, Dieter Schuh, Dominique Bougeard, Andreas D Wieck, and Hendrik Bluhm. Closed-loop control of a GaAs-based singlet-triplet spin qubit with 99.5% gate fidelity and low leakage. Nature Communications, 11 (1): 1–6, 2020. 10.1038/​s41467-020-17865-3.

[6] Akito Noiri, Kenta Takeda, Takashi Nakajima, Takashi Kobayashi, Amir Sammak, Giordano Scappucci, and Seigo Tarucha. Fast universal quantum gate above the fault-tolerance threshold in silicon. Nature, 601 (7893): 338–342, 2022. 10.1038/​s41586-021-04182-y.

[7] Stephan GJ Philips, Mateusz T Mądzik, Sergey V Amitonov, Sander L de Snoo, Maximilian Russ, Nima Kalhor, Christian Volk, William IL Lawrie, Delphine Brousse, Larysa Tryputen, et al. Universal control of a six-qubit quantum processor in silicon. Nature, 609 (7929): 919–924, 2022. 10.1038/​s41586-022-05117-x.

[8] Federico Fedele, Anasua Chatterjee, Saeed Fallahi, Geoffrey C Gardner, Michael J Manfra, and Ferdinand Kuemmeth. Simultaneous operations in a two-dimensional array of singlet-triplet qubits. PRX Quantum, 2 (4): 040306, 2021. 10.1103/​PRXQuantum.2.040306.

[9] Luca Petit, HGJ Eenink, M Russ, WIL Lawrie, NW Hendrickx, SGJ Philips, JS Clarke, LMK Vandersypen, and M Veldhorst. Universal quantum logic in hot silicon qubits. Nature, 580 (7803): 355–359, 2020. 10.1038/​s41586-020-2170-7.

[10] Chih Heng Yang, RCC Leon, JCC Hwang, Andre Saraiva, Tuomo Tanttu, Wister Huang, J Camirand Lemyre, Kok Wai Chan, KY Tan, Fay E Hudson, et al. Operation of a silicon quantum processor unit cell above one kelvin. Nature, 580 (7803): 350–354, 2020. 10.1038/​s41586-020-2171-6.

[11] Leon C Camenzind, Simon Geyer, Andreas Fuhrer, Richard J Warburton, Dominik M Zumbühl, and Andreas V Kuhlmann. A hole spin qubit in a fin field-effect transistor above 4 kelvin. Nature Electronics, 5 (3): 178–183, 2022. 10.1038/​s41928-022-00722-0.

[12] Ronald Hanson, Leo P Kouwenhoven, Jason R Petta, Seigo Tarucha, and Lieven MK Vandersypen. Spins in few-electron quantum dots. Reviews of Modern Physics, 79 (4): 1217, 2007. 10.1103/​RevModPhys.79.1217.

[13] Luca Petit, Maximilian Russ, Gertjan HGJ Eenink, William IL Lawrie, James S Clarke, Lieven MK Vandersypen, and Menno Veldhorst. Design and integration of single-qubit rotations and two-qubit gates in silicon above one kelvin. Communications Materials, 3 (1): 82, 2022. 10.1038/​s43246-022-00304-9.

[14] J Darulová, SJ Pauka, N Wiebe, KW Chan, GC Gardener, MJ Manfra, MC Cassidy, and Matthias Troyer. Autonomous tuning and charge-state detection of gate-defined quantum dots. Physical Review Applied, 13 (5): 054005, 2020. 10.1103/​PhysRevApplied.13.054005.

[15] H Moon, DT Lennon, J Kirkpatrick, NM van Esbroeck, LC Camenzind, Liuqi Yu, F Vigneau, DM Zumbühl, G Andrew D Briggs, MA Osborne, et al. Machine learning enables completely automatic tuning of a quantum device faster than human experts. Nature Communications, 11 (1): 1–10, 2020. 10.1038/​s41467-020-17835-9.

[16] Brandon Severin, Dominic T Lennon, Leon C Camenzind, Florian Vigneau, F Fedele, D Jirovec, A Ballabio, D Chrastina, G Isella, M de Kruijf, et al. Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. arXiv preprint arXiv:2107.12975, 2021. 10.48550/​arXiv.2107.12975.

[17] Timothy A Baart, Pieter T Eendebak, Christian Reichl, Werner Wegscheider, and Lieven MK Vandersypen. Computer-automated tuning of semiconductor double quantum dots into the single-electron regime. Applied Physics Letters, 108 (21): 213104, 2016. 10.1063/​1.4952624.

[18] Sandesh S Kalantre, Justyna P Zwolak, Stephen Ragole, Xingyao Wu, Neil M Zimmerman, Michael D Stewart, and Jacob M Taylor. Machine learning techniques for state recognition and auto-tuning in quantum dots. npj Quantum Information, 5 (1): 1–10, 2019. 10.1038/​s41534-018-0118-7.

[19] Justyna P Zwolak, Thomas McJunkin, Sandesh S Kalantre, JP Dodson, ER MacQuarrie, DE Savage, MG Lagally, SN Coppersmith, Mark A Eriksson, and Jacob M Taylor. Autotuning of double-dot devices in situ with machine learning. Physical Review Applied, 13 (3): 034075, 2020. 10.1103/​PhysRevApplied.13.034075.

[20] V Nguyen, SB Orbell, Dominic T Lennon, Hyungil Moon, Florian Vigneau, Leon C Camenzind, Liuqi Yu, Dominik M Zumbühl, G Andrew D Briggs, Michael A Osborne, et al. Deep reinforcement learning for efficient measurement of quantum devices. npj Quantum Information, 7 (1): 1–9, 2021. 10.1038/​s41534-021-00434-x.

[21] Justyna P Zwolak, Thomas McJunkin, Sandesh S Kalantre, Samuel F Neyens, ER MacQuarrie, Mark A Eriksson, and Jacob M Taylor. Ray-based framework for state identification in quantum dot devices. PRX Quantum, 2 (2): 020335, 2021. 10.1103/​PRXQuantum.2.020335.

[22] NM van Esbroeck, DT Lennon, H Moon, V Nguyen, F Vigneau, LC Camenzind, L Yu, DM Zumbühl, GAD Briggs, Dino Sejdinovic, et al. Quantum device fine-tuning using unsupervised embedding learning. New Journal of Physics, 22 (9): 095003, 2020. 10.1088/​1367-2630/​abb64c.

[23] Julian D Teske, Simon Sebastian Humpohl, René Otten, Patrick Bethke, Pascal Cerfontaine, Jonas Dedden, Arne Ludwig, Andreas D Wieck, and Hendrik Bluhm. A machine learning approach for automated fine-tuning of semiconductor spin qubits. Applied Physics Letters, 114 (13): 133102, 2019. 10.1063/​1.5088412.

[24] CJ Van Diepen, Pieter T Eendebak, Bruno T Buijtendorp, Uditendu Mukhopadhyay, Takafumi Fujita, Christian Reichl, Werner Wegscheider, and Lieven MK Vandersypen. Automated tuning of inter-dot tunnel coupling in double quantum dots. Applied Physics Letters, 113 (3): 033101, 2018. 10.1063/​1.5031034.

[25] Tim Botzem, Michael D Shulman, Sandra Foletti, Shannon P Harvey, Oliver E Dial, Patrick Bethke, Pascal Cerfontaine, Robert PG McNeil, Diana Mahalu, Vladimir Umansky, et al. Tuning methods for semiconductor spin qubits. Physical Review Applied, 10 (5): 054026, 2018. 10.1103/​PhysRevApplied.10.054026.

[26] David L Craig, Hyungil Moon, Federico Fedele, Dominic T Lennon, Barnaby van Straaten, Florian Vigneau, Leon C Camenzind, Dominik M Zumbühl, G Andrew D Briggs, Michael A Osborne, Dino Seijdinovic, and Natalia Ares. Bridging the reality gap in quantum devices with physics-aware machine learning. arXiv preprint arXiv:2111.11285, 2021. 10.48550/​arXiv.2111.11285.

[27] Stefanie Czischek, Victor Yon, Marc-Antoine Genest, Marc-Antoine Roux, Sophie Rochette, Julien Camirand Lemyre, Mathieu Moras, Michel Pioro-Ladrière, Dominique Drouin, Yann Beilliard, et al. Miniaturizing neural networks for charge state autotuning in quantum dots. Machine Learning: Science and Technology, 3 (1): 015001, 2021. 10.1088/​2632-2153/​ac34db.

[28] Renato Durrer, Benedikt Kratochwil, Jonne V Koski, Andreas J Landig, Christian Reichl, Werner Wegscheider, Thomas Ihn, and Eliska Greplova. Automated tuning of double quantum dots into specific charge states using neural networks. Physical Review Applied, 13 (5): 054019, 2020. 10.1103/​PhysRevApplied.13.054019.

[29] Maxime Lapointe-Major, Olivier Germain, J Camirand Lemyre, Dany Lachance-Quirion, Sophie Rochette, F Camirand Lemyre, and Michel Pioro-Ladrière. Algorithm for automated tuning of a quantum dot into the single-electron regime. Physical Review B, 102 (8): 085301, 2020. 10.1103/​PhysRevB.102.085301.

[30] Yuta Matsumoto, Takafumi Fujita, Arne Ludwig, Andreas D Wieck, Kazunori Komatani, and Akira Oiwa. Noise-robust classification of single-shot electron spin readouts using a deep neural network. npj Quantum Information, 7 (1): 1–7, 2021. 10.1038/​s41534-021-00470-7.

[31] Jana Darulová, Matthias Troyer, and Maja C Cassidy. Evaluation of synthetic and experimental training data in supervised machine learning applied to charge-state detection of quantum dots. Machine Learning: Science and Technology, 2021. 10.1088/​2632-2153/​ac104c.

[32] Simon Geyer, Leon C Camenzind, Lukas Czornomaz, Veeresh Deshpande, Andreas Fuhrer, Richard J Warburton, Dominik M Zumbühl, and Andreas V Kuhlmann. Self-aligned gates for scalable silicon quantum computing. Applied Physics Letters, 118 (10): 104004, 2021. 10.1063/​5.0036520.

[33] Franck HL Koppens, Joshua A Folk, Jeroen M Elzerman, Ronald Hanson, LH Willems Van Beveren, Ivo T Vink, Hans-Peter Tranitz, Werner Wegscheider, Leo P Kouwenhoven, and Lieven MK Vandersypen. Control and detection of singlet-triplet mixing in a random nuclear field. Science, 309 (5739): 1346–1350, 2005. 10.1126/​science.1113719.

[34] Matthias Brauns, Joost Ridderbos, Ang Li, Erik PAM Bakkers, Wilfred G Van Der Wiel, and Floris A Zwanenburg. Anisotropic pauli spin blockade in hole quantum dots. Physical Review B, 94 (4): 041411, 2016. 10.1103/​PhysRevB.94.041411.

[35] J Danon and Yu V Nazarov. Pauli spin blockade in the presence of strong spin-orbit coupling. Physical Review B, 80 (4): 041301, 2009. 10.1103/​PhysRevB.80.041301.

[36] S Nadj-Perge, SM Frolov, JWW Van Tilburg, J Danon, Yu V Nazarov, R Algra, EPAM Bakkers, and LP Kouwenhoven. Disentangling the effects of spin-orbit and hyperfine interactions on spin blockade. Physical Review B, 81 (20): 201305, 2010. 10.1103/​PhysRevB.81.201305.

[37] Ruoyu Li, Fay E Hudson, Andrew S Dzurak, and Alexander R Hamilton. Pauli spin blockade of heavy holes in a silicon double quantum dot. Nano Letters, 15 (11): 7314–7318, 2015. 10.1021/​acs.nanolett.5b02561.

[38] FNM Froning, MJ Rančić, B Hetényi, S Bosco, MK Rehmann, Ang Li, Erik PAM Bakkers, Floris Arnoud Zwanenburg, Daniel Loss, DM Zumbühl, et al. Strong spin-orbit interaction and g-factor renormalization of hole spins in Ge/​Si nanowire quantum dots. Physical Review Research, 3 (1): 013081, 2021. 10.1103/​PhysRevResearch.3.013081.

[39] TH Stoof and Yu V Nazarov. Time-dependent resonant tunneling via two discrete states. Physical Review B, 53 (3): 1050, 1996. 10.1103/​PhysRevB.53.1050.

[40] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 10.1109/​CVPR.2016.90.

[41] TorchVision maintainers and contributors. Torchvision: Pytorch's computer vision library. https:/​/​​pytorch/​vision, 2016.

[42] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 (11): 2278–2324, 1998. 10.1109/​5.726791.

[43] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 10.48550/​arXiv.1412.6980.

[44] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019. 10.48550/​arXiv.1912.01703.

Cited by

[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).

The above citations are from SAO/NASA ADS (last updated successfully 2023-09-22 12:44:40). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref's cited-by service no data on citing works was found (last attempt 2023-09-22 12:44:38).