最小基塔耶夫链的跨平台自主控制

David van Driel, Rouven Koch, Vincent P. M. Sietses, Sebastiaan L. D. ten Haaf, Chun-Xiao Liu, Francesco Zatelli, Bart Roovers, Alberto Bordin, Nick van Loo, Guanzhong Wang, Jan Cornelis Wolff, Grzegorz P. Mazur, Tom Dvir, Ivan Kulesh, Qingzhen Wang, A. Mert Bozkurt, Sasa Gazibegovic, Ghada Badawy, Erik P. A. M. Bakkers, Michael Wimmer, Srijit Goswami, Jose L. Lado, Leo P. Kouwenhoven, Eliska Greplova
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引用次数: 0

摘要

当代量子设备在尺寸和复杂性上都达到了新的极限,从而可以对新出现的量子模式进行实验探索。然而,复杂性的增加给器件调谐和控制带来了巨大挑战。在这里,我们展示了在基塔耶夫链的最小实现中自主调谐出现的马约拉纳零模。我们利用跨平台迁移学习实现了这一任务。首先,我们在理论模型上训练调谐模型。接着,我们使用二维电子气中的基塔耶夫链实现对其进行再训练。最后,我们将该模型应用于调整量子点中通过一维纳米线中的半导体-超导体部分耦合实现的基塔埃夫链。利用卷积神经网络,我们从差分电导测量结果中预测了隧穿和库珀对分裂率,并利用这些预测结果将电化学势调整到马约拉纳甜点。该算法成功收敛到甜点附近(67.6% 的尝试收敛在 1.5 mV 以内,80.9% 的尝试收敛在 4.5 mV 以内),通常在 45 分钟或更短时间内找到甜点。这一进展是迈向自主调整相互作用系统中出现的模式,以及迈向可在一系列实验平台上部署的基本调整机器学习模型的垫脚石。
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Cross-Platform Autonomous Control of Minimal Kitaev Chains
Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here, we demonstrate autonomous tuning of emergent Majorana zero modes in a minimal realization of a Kitaev chain. We achieve this task using cross-platform transfer learning. First, we train a tuning model on a theory model. Next, we retrain it using a Kitaev chain realization in a two-dimensional electron gas. Finally, we apply this model to tune a Kitaev chain realized in quantum dots coupled through a semiconductor-superconductor section in a one-dimensional nanowire. Utilizing a convolutional neural network, we predict the tunneling and Cooper pair splitting rates from differential conductance measurements, employing these predictions to adjust the electrochemical potential to a Majorana sweet spot. The algorithm successfully converges to the immediate vicinity of a sweet spot (within 1.5 mV in 67.6% of attempts and within 4.5 mV in 80.9% of cases), typically finding a sweet spot in 45 minutes or less. This advancement is a stepping stone towards autonomous tuning of emergent modes in interacting systems, and towards foundational tuning machine learning models that can be deployed across a range of experimental platforms.
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