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.

翻译：我们提出一种基于神经网络的模型，该模型能够学习量子点模拟器中工作状态的广阔参数空间，并利用该知识基于输运测量对这些设备进行自动调谐，以在结构中实现马约拉纳模式。该模型以电导图谱形式的合成数据进行无监督训练，采用融入马约拉纳零模关键特性的物理信息损失函数。研究表明，经过适当训练后，深度视觉Transformer网络能够有效记忆哈密顿量参数与电导图谱结构之间的关联关系，并利用该关联为量子点链提出驱动系统进入拓扑相的参数更新方案。从参数空间中大范围的初始失谐出发，仅需单次更新步骤即可产生非平庸的零模。此外，通过启用迭代调谐程序——系统在每一步获取更新的电导图谱——我们证明该方法能够处理参数空间中更广阔的区域。