Filippo Miatto
Quantum Machine Learning Researcher at Xanadu
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolFilippo Miatto is a Quantum Machine Learning Researcher with nine years of experience bridging quantum optics, computational physics, and machine learning, currently working as Principal Quantum Architecture Scientist at Xanadu in Toronto. He combines deep academic training—a PhD in Quantum Optics and postdoctoral roles at Waterloo and Ottawa—with applied research building software for continuous-variable quantum circuits, notably contributing backend utilities and tests to the widely used Strawberry Fields library. An effective communicator and educator, he excels at translating complex quantum information concepts for non-specialists and has taught machine learning and quantum computing at Télécom Paris. His work blends hands-on code quality improvements and algorithmic design with experimental intuition from photon entanglement research, giving him a rare mix of theoretical, experimental, and software expertise.
9 years of coding experience
3 years of employment as a software developer
M.Sc., Physics, M.Sc., Physics at Università degli Studi di PadovaPh.D., Quantum Optics, Ph.D., Quantum Optics at University of StrathclydeErasmus, Physics, Erasmus, Physics at Leiden University
English
Stackoverflow
1,874reputation
174kreached
7answers
60questions
Github Skills (13)
quantum-computing10
python10
numpy10
photonics9
machine-learning8
tensorflow8
cron6
pytorch6
stride6
jupyter-notebook6
bash6
matplotlib6
jupyter6
Programming languages (15)
Github contributions (5)
XanaduAI/strawberryfields
Feb 2019 - Jul 2020
Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing continuous variable (CV) quantum optical circuits.
Role in this project:
Backend Developer
Backend DeveloperContributions:29 reviews, 35 commits, 12 PRs in 1 year 4 months
Contributions summary:Filippo primarily contributed to the `strawberryfields` library by implementing and refining utility functions. Their work involved the creation of methods to extract unitary and channel representations from quantum circuits, including functions for vectorizing and unvectorizing density matrices. Furthermore, the user refactored existing code, replacing f-strings with string formatting and implementing pylint recommendations for improved code quality. This included adding tests for the implemented functions and incorporating examples in docstrings and comments for better clarity.
pythoncircuitstensorflowoptimizingquantum-computing
ziofil/INFPHY201
Sep 2020 - Dec 2024
Contributions:10 pushes, 1 branch in 4 years 3 months
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