13 citations · 33 across the 11 of their papers we have counts for
11 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
Identifying Majorana zero modes in semiconductor--superconductor nanowires requires ultra-low temperature transport measurements in dilution refrigerators, making device screening…
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…
Machine learning Majorana topology using unsupervised and supervised learning
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…
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…
Mitigating disorder and optimizing topological indicators with vision-transformer-based neural networks in Majorana nanowires
Jacob R. Taylor, Sankar Das Sarma
Disorder remains a major obstacle to realizing topological Majorana zero modes (MZMs) in superconductor-semiconductor nanowires, and we show how deep learning can be used to recove…
Vision transformer based Deep Learning of Topological indicators in Majorana Nanowires
Jacob R. Taylor, Sankar Das Sarma
1D superconductor-semiconductor nanowires are the leading candidates for topological quantum computation due to their ability to host non-Abelian Majorana zero modes (MZMs). Howeve…
Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control
Jacob R. Taylor, Sankar Das Sarma
Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physi…
Assessing quantum dot SWAP gate fidelity using tensor network methods
Jacob R. Taylor, Nathan L. Foulk, Sankar Das Sarma
Advanced tensor network numerical methods are used to explore the fidelity of repeated SWAP operations on a system comprising 20-100 quantum dot spin qubits in the presence of vall…
Machine Learning the Disorder Landscape of Majorana Nanowires
Jacob R. Taylor, Jay D. Sau, Sankar Das Sarma
We develop a practical machine learning approach to determine the disorder landscape of Majorana nanowires by using training of the conductance matrix and inverting the conductance…