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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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About me

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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.

Latest Publications

Mitigating disorder and optimizing topological indicators with vision-transformer-based neural networks in Majorana nanowires

7/10/2025
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 recover topological MZMs mitigating disorder even when the pre-mitigation situation manifests no apparent topology. The disorder potential, as well as the scattering invariant ($T_V$) normally used to classify a device as topologically non-trivial are not directly measurable experimentally. Additionally, the conventional signatures of MZMs have proved insufficient due to their being accidentally replicated by disorder-induced trivial states. Recent advances in machine learning provide a novel method to solve these problems, allowing the underlying topology, suppressed by disorder, to be recovered using effective mitigation procedures. In this work, we leverage a vision transformer neural network trained on conductance measurements along with a CMA-ES optimization framework to dynamically tune gate voltages mitigating disorder effects. Unlike prior efforts that relied on indirect cost functions, our method directly optimizes $T_V$ alongside additional local density of states-based topological indicators. Using a lightweight neural network variant, we demonstrate that even highly disordered nanowires initially lacking any topologically non-trivial regions can be transformed into robust topological devices.

Vision transformer based deep learning of topological indicators in Majorana nanowires

3/28/2025
1D superconductor-semiconductor nanowires are the leading candidates for topological quantum computation due to their ability to host non-Abelian Majorana zero modes (MZMs). However, the standard methods for identifying MZMs are often inadequate, particularly in the presence of disorder, where many properties considered to be heralds of MZMs are often generated by trivial disorder induced Andreev bound states. Recent works clearly indicate the need for developing new techniques for identifying and diagnosing MZMs. In this study, we utilize a generalized Vision Transformer-based neural network to predict, using tunnel conductance measurements, both whether a device manifests a topological MZMs phase in the presence of disorder, and also to map out the entire topological phase diagram. We show the ability of our method up to arbitrary confidence ($P>0.9998$) in classifying a device as possessing a non-trivial MZM-carrying topological phase for a wide variety of disorder parameters. We demonstrate an ability to predict from conductance measurements alternative (to the extensively used scattering-matrix-invariant topological indicator) Majorana indicators based on local density of states (LDOS). This is relevant since topology may not be uniquely defined by the scattering invariant in short disordered wires. This work serves as a significant advance offering a step towards the practical realization of Majorana-based quantum devices, enabling a deep-learning understanding of the topological properties in disordered nanowire systems. We validate our method using extensive simulated Majorana results in the presence of disorder, and suggest using this technique for the analysis of experimental data in superconductor-semiconductor hybrid Majorana platforms.

Work Experience

Mar 2025 – Present

Microsoft

Quantum Engineer Intern

Internship focused on quantum engineering.

Aug 2022 – Present

University of Maryland

Graduate Research Assistant

Full-time graduate research assistantship.

Jun 2020 – Aug 2022

University of Waterloo

Graduate Research Assistant

Full-time graduate research role working on physics research projects.

Sep 2019 – Sep 2020

University of Waterloo

Undergraduate Research Assistant

Internship contributing to research in physics and quantum computing.

Jan 2019 – Aug 2019

National Research Council Canada

Research Assistant

Internship research assistant role.

May 2018 – Aug 2018

National Research Council Canada

Research Assistant

Internship research assistant role.

Sep 2017 – Dec 2017

TELUS

Software Developer Intern

Internship in software development.

Jan 2017 – Apr 2017

Independent Electricity System Operator (IESO)

Market Analyst Intern

Mostly turned into software development for database management and automating daily tasks.

Latest Posts

Getting Started with Spectre

12/29/2024
Find out how to configure Spectre.

Latest Projects

Spectre

12/29/2024
Spectre is a terminal-inspired theme for Astro, built using Astro and TypeScript.