Majorana bound state engineering via efficient real-space parameter optimization (Conference Presentation)

S. Boutin, J. C. Lemyre, I. Garate
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引用次数: 12

Abstract

Recent progress toward the fabrication of Majorana-based qubits has sparked the need for systematic approaches to optimize experimentally relevant parameters for the realization of robust Majorana bound states. Here, we introduce an efficient numerical method for the real-space optimization of tunable parameters, such as electrostatic potential profiles and magnetic field textures, in Majorana wires. Combining ideas from quantum control and quantum transport, our algorithm, applicable to any noninteracting tight-binding model, operates on a largely unexplored parameter space and opens new routes for Majorana bound states with enhanced robustness. Contrary to common belief, we find that spatial inhomogeneities of parameters can be a resource for the engineering of Majorana bound states.
基于高效实空间参数优化的马约拉纳束缚态工程(会议报告)
最近基于马约拉纳的量子比特的制造进展引发了对系统方法的需求,以优化实验相关参数,以实现鲁棒马约拉纳束缚态。本文介绍了一种有效的数值方法,用于马约拉纳线中静电电位分布和磁场结构等可调参数的实空间优化。结合量子控制和量子输运的思想,我们的算法适用于任何非相互作用的紧密结合模型,在很大程度上未被探索的参数空间上运行,并以增强的鲁棒性为Majorana束缚态开辟了新的路线。与通常的看法相反,我们发现参数的空间非均质性可以成为马约拉纳束缚态工程的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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