Machine learning optimization of Majorana hybrid nanowires
This work addresses the challenge of expensive manual tuning in quantum systems like Majorana wires, offering an automated solution for researchers in quantum computing, though it appears incremental as it applies an existing algorithm to a specific domain.
The researchers tackled the problem of automating the tuning of gate arrays in Majorana hybrid nanowires with strong disorder, using the CMA-ES machine learning algorithm. They found that the algorithm efficiently improved topological signatures, learned intrinsic disorder profiles, and fully recovered Majorana zero modes destroyed by disorder, achieving this with only 20 gates.
As the complexity of quantum systems such as quantum bit arrays increases, efforts to automate expensive tuning are increasingly worthwhile. We investigate machine learning based tuning of gate arrays using the CMA-ES algorithm for the case study of Majorana wires with strong disorder. We find that the algorithm is able to efficiently improve the topological signatures, learn intrinsic disorder profiles, and completely eliminate disorder effects. For example, with only 20 gates, it is possible to fully recover Majorana zero modes destroyed by disorder by optimizing gate voltages.