From the 1 of 9 linked papers with an AI index.
9 papers
Avoiding Dilution: Using Diffusion and Vision Transformers to resolve Majorana Features in Nanowires at High Temperature
Jacob R. Taylor, Haining Pan, Jay D. Sau +1
The paper shows that neural networks based on diffusion-inspired U‑Net transformers and vision transformers can reconstruct low‑temperature conductance and predict topological visi…
Towards a microscopic model for an electronic quantum charge liquid
Jacob R. Taylor, Sankar Das Sarma, Seth Musser
We provide a route to constructing an electronic quantum charge liquid (QCL), a state made up of fermions at fractional filling of a lattice that does not break translation. Starti…
Large Scale Optimization of Disordered Hubbard Models through Tensor and Neural Networks
Jacob R. Taylor, Sankar Das Sarma
We theoretically demonstrate a practical method for tuning randomly disordered 2D quantum-dot grids underlying spin qubit platforms using vision-based neural networks trained on te…
Predicting spin-orbit coupling in hole spin qubit arrays with vision-transformer-based neural networks on a generalized Hubbard model
Jacob R. Taylor, Katharina Laubscher, Sankar Das Sarma
We introduce a neural-network-based machine learning method to predict the effective spin-orbit coupling (SOC) strength in hole quantum dot arrays from standard charge stability di…
Ballistic transport in 1D Rashba systems in the context of Majorana nanowires
Haining Pan, Jacob R. Taylor, Jay D. Sau +1
Recent work on Majorana-bound states in semiconductor-superconductor hybrid structures has elucidated the key role of unintentional (and unknown) disorder (producing low-energy And…
Unreasonable effectiveness of unsupervised learning in identifying Majorana topology
Jacob Taylor, Haining Pan, Sankar Das Sarma
In unsupervised learning, the training data for deep learning does not come with any labels, thus forcing the algorithm to discover hidden patterns in the data for discerning usefu…