Machine learning detecting Majorana Zero Mode from Zero Bias Peak measurements

Majorana zero modes (MZMs), emerging as exotic quasiparticles that carry non-Abelian statistics, hold great promise for achieving fault-tolerant topological quantum computation. A key signature of the presence of MZMs is the zero-bias peaks (ZBPs) from tunneling differential conductance. However, the identification of MZMs from ZBPs has faced tremendous challenges, due to the presence of topological trivial states that generate spurious ZBP signals. In this work, we introduce a machine-learning framework that can discern MZM from other signals using ZBP data. Quantum transport simulation from tight-binding models is used to generate the training data, while persistent cohomology analysis confirms the feasibility of classification via machine learning. In particular, even with added data noise, XGBoost classifier reaches $85\%$ accuracy for 1D tunneling conductance data and $94\%$ for 2D data incorporating Zeeman splitting. Tests on prior ZBP experiments show that some data are more likely to originate from MZM than others. Our model offers a quantitative approach to assess MZMs using ZBP data. Furthermore, our results shed light on the use of machine learning on exotic quantum systems with experimental-computational integration

Topological superconductors from a materials perspective

Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits that are robust against local perturbations, TSCs offer a promising platform toward (non-universal) topological quantum computation. However, there has been a scarcity of TSC candidates, and the experimental signatures that identify a TSC are often elusive. In this perspective, after a short review of the TSC basics and theories, we provide an overview of the TSC materials candidates, including natural compounds and synthetic material systems. We further introduce various experimental techniques to probe TSC, focusing on how a system is identified as a TSC candidate, and why a conclusive answer is often challenging to draw. We conclude by calling for new experimental signatures and stronger computational support to accelerate the search for new TSC candidates.Comment: 42 pages, 6 figure

Tunable Three-Dimensional Photonic Crystal Microwave Resonator with Ultra-Small Mode Volume

Microwave resonator is an important electromagnetic tool with applications in many areas, including communication, nonlinear optics, cavity quantum electrodynamics, and precision metrology. In this work, we did simulations and experiments on a design of three-dimensional (3D) dielectric photonic crystal (PhC) cavity (PhCC) based on ABCD woodpile structure. Low material loss of the dielectric and radiation loss suppression of the 3D PhC’s complete bandgap give PhCC high quality factor (). The design also includes field focusing structure into the PhCC to achieve ultrasmall effective mode volume (ₑ subscript ). Moreover, the topology of the woodpile structure allows us to tune the PhCC’s resonance frequency as well. Our simulation results agree with the experiments’ on the band structure of the PhC, , and the tunability of the PhCC. Furthermore, the simulation shows that we can achieve indefinitely small ₑ subscript by the field focusing structure limited only by the fabrication resolution. Our experiments on the design’s 6 × 6 × 5 unit-cell PhCC demos give a complete bandgap between 3.6 GHz and 4.1 GHz. We measured their to be in the order of 10² for the resonance inside the complete bandgap regardless of the existence of the field focusing structure which normally cause high radiation loss. We also can tune the resonance of the PhCC up to 64% of the width of the complete bandgap.S.B