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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 at the intersection of artificial intelligence, machine learning, condensed matter physics, and quantum information. My research focuses on developing and applying advanced machine learning techniques, particularly deep learning and neural networks, to solve challenging problems in quantum science. My work involves extensive use of deep learning architectures, including vision transformers, large-scale numerical simulations, numerical optimization, and tensor networks to understand, characterize, and control complex quantum systems. My research spans quantum-dot devices, many-body quantum systems, and Majorana zero modes for topological quantum computing. More broadly, I am interested in leveraging artificial intelligence and computational methods to advance scientific discovery and develop next-generation quantum technologies.

Latest Publications

Avoiding Dilution: Using Diffusion and Vision Transformers to resolve Majorana Features in Nanowires at High Temperature

10/1/2026
Identifying Majorana zero modes in semiconductor-superconductor nanowires requires ultra-low temperature transport measurements in dilution refrigerators, making device screening slow and resource-intensive. Here, we investigate whether high-temperature conductance data can be used to infer low-temperature Majorana nanowire properties before committing devices to dilution-refrigerator characterization. We generate paired high- and low-temperature conductance simulations for disordered Majorana nanowires and train neural networks to perform two related tasks. First, we use a Shifted Window U-Net Transformer diffusion-inspired architecture to reconstruct low-temperature conductance from thermally broadened high-temperature measurements, achieving high-fidelity recovery with $R^2 \approx 0.95$ for local conductance and $R^2 \approx 0.91$ for nonlocal conductance. Second, we train a Video Vision Transformer-based network to predict the low-temperature topological visibility directly from high-temperature conductance, obtaining $R^2 \approx 0.80$. These results demonstrate that machine-learning models can recover and infer low-temperature Majorana features from experimentally easier high-temperature data, providing a practical route for rejecting poor devices early thus avoiding slow and resource-intensive dilution refrigeration for non-promising devices. This high-temperature screening approach could substantially accelerate the experimental feedback loop for Majorana nanowire device development.

Machine learning Majorana topology using unsupervised and supervised learning

9/21/2026
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 useful information. This, in principle, could be a powerful tool in identifying topological order since topology does not always manifest in obvious physical ways (e.g., topological superconductivity) for its decisive confirmation. The problem, however, is that unsupervised learning is a difficult challenge, necessitating huge computing resources, which may not always work. In the current work, we combine unsupervised and supervised learning to establish that unlabeled (simulated) data in the Majorana splitting in realistic short disordered nanowires may enable not only a distinction between 'topological' and 'trivial', but also where their crossover happens in the relevant parameter space. This may be a useful tool in identifying topology in experimental Majorana nanowires.

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.