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Jacob Taylor

PhD Physics Candidate at the University of Maryland, College Park
MSc Quantum Computing at the University of Waterloo
BSc Math and Physics at the University of Waterloo

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Hello there My name is Jacob R. Taylor, and I am a Physics PhD student at the University of Maryland (UMD), specializing in research that intersects condensed matter physics, quantum information, and machine learning. My work primarily involves intensive numerical simulations and machine learning techniques. I focus on using deep learning to analyze disorder in quantum devices and employ tensor networks to investigate many-body quantum systems. I am particularly interested in Majorana particles and their potential to contribute to the development of topological quantum computers.

Machine learning Majorana nanowire disorder landscape

This article presents a machine-learning approach to reconstruct the disorder landscape of Majorana nanowires by training on the tunnelling conductance matrix. Any measured conductance profile can be inverted to uniquely extract disorder parameters, allowing the determination of topological invariants and the structure of Majorana wave-functions. The work opens a new pathway for diagnosing and optimizing Majorana devices directly from experimental conductance data.

Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control

Machine learning offers a powerful avenue for improving noisy quantum devices. This work introduces a convolutional neural network that learns the disorder landscape of a disordered extended Hubbard model directly from charge-stability diagrams. By processing site-specific disorder in hopping terms, on-site potentials and Coulomb interactions simultaneously, the network predicts disorder parameters with very high accuracy and reduces the number of constraints compared with previous approaches. The method enables automated tuning of five or more quantum dots and mitigates crosstalk between device parameters.

2024 - present

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