Jay Soni
Senior Quantum Software Developer at Xanadu
Brampton, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolJay Soni is a Senior Quantum Software Developer with nine years of experience building quantum and ML-enabled tooling, currently leading software efforts at Xanadu from Brampton, Ontario. He has contributed significant back-end and ML engineering work to PennyLane, enhancing circuit sampling, measurement semantics, and test coverage for a widely used open-source quantum computing framework. His background spans hands-on research—interfacing defect centers in diamond at Harvard—and applied roles in medical physics and computer vision, giving him a rare mix of experimental, algorithmic, and production engineering expertise. Jay combines deep mathematical physics training from the University of Waterloo with pragmatic software design, consistently translating research-grade quantum concepts into robust developer-facing APIs and tutorials.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BSc, Honours Mathematical Physics, Bachelor of Science - BSc, Honours Mathematical Physics at University of Waterloo
Github Skills (19)
unit-testing10
pytorch10
learn-ruby-on-rails10
python10
user-manual10
machine-learning10
basics10
automatic-differentiation10
tensorflow10
quantum-computing10
qml10
q-learning10
differentiable-programming9
optimization8
quantum-chemistry8
Programming languages (4)
Github contributions (5)
PennyLaneAI/pennylane
Jul 2021 - Jan 2023
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:2 releases, 1856 reviews, 263 commits in 1 year 5 months
Contributions summary:Jay's commits primarily focused on enhancing the functionality of the PennyLane library, a Python library for quantum computing and machine learning. They added and refined features related to quantum circuit sampling and measurement, including the ability to return raw samples and to correctly account for shot ranges. These changes involved modifying core files and adding comprehensive unit tests. Additionally, the user was involved in refactoring the code by removing specific template arguments.
pythonautomatic-differentiationdifferentiable-computingcomputerstensorflow
PennyLaneAI/qml
Sep 2021 - Dec 2022
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
Role in this project:ML Engineer
ML EngineerContributions:237 reviews, 20 commits, 47 PRs in 1 year 3 months
Contributions summary:Jay's commits primarily focused on updating and refactoring tutorial demonstrations within the quantum machine learning framework. They integrated the new `qml.sample` and `qml.expval(H)` functionalities across multiple tutorials, including those for QAOA, VQLS, and chemical reactions. Additionally, the user addressed bug fixes and CI/CD-related issues, improving the usability and functionality of the tutorials for other contributors.
automatic-differentiationtensorflowkey-conceptsqqml
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