用AI自动调参，让量子点系统快速进入拓扑态并产生马约拉纳零模。
AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
- 基于视觉变换器的神经网络，从导电图谱中学习哈密顿量参数与拓扑态的关系。
- 仅需一次参数更新，即可在广泛初始条件下实现非平庸零模。
- 支持迭代优化，可覆盖更大参数空间，适合实验物理与量子器件开发人员。
我们提出一种基于神经网络的模型，能够学习量子点模拟器中工作区域的广泛分布，并利用该知识通过输运测量自动调节这些器件，以实现结构中的马约拉纳零模。该模型在合成数据（导电图谱形式）上进行无监督训练，采用包含马约拉纳零模关键特性的物理信息损失函数。结果表明，经过适当训练，深度视觉变换器网络能高效记忆哈密顿量参数与导电图谱之间的关系，并据此为量子点链提出参数更新方案，引导系统进入拓扑相。从参数空间中广泛的初始失谐状态出发，单次更新即可生成非平庸零模。此外，通过在每一步获取新的导电图谱并进行迭代调参，该方法可处理更广阔的参数区域。
原文摘要 · Abstract (English)
We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this knowledge to autotune these devices - based on transport measurements - toward obtaining Majorana modes in the structure. The model is trained in an unsupervised manner on synthetic data in the form of conductance maps, using a physics-informed loss that incorporates key properties of Majorana zero modes. We show that, with appropriate training, a deep vision-transformer network can efficiently memorize relation between Hamiltonian parameters and structures on conductance maps and use it to propose parameters update for a quantum dot chain that drive the system toward topological phase. Starting from a broad range of initial detunings in parameter space, a single update step is sufficient to generate nontrivial zero modes. Moreover, by enabling an iterative tuning procedure - where the system acquires updated conductance maps at each step - we demonstrate that the method can address a much larger region of the parameter space.
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