David Ittah
Senior Quantum Software Developer - Technical Lead at Xanadu
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolDavid Ittah is a senior quantum software developer and technical lead with seven years of experience building quantum compilers and ML-enabled quantum software, currently leading the compiler team at Xanadu. He combines deep academic research—an ETH Zürich MSc with published work on SSA-like quantum IRs and MLIR implementations—with hands-on engineering in Python, C++, and compiler tooling. As a lead contributor to PennyLane and a developer on Microsoft's Q# compiler examples, he has shipped JIT compilation prototypes and production-ready improvements to state preparation and embedding templates that accelerate quantum ML workflows. David excels at bridging classical compiler infrastructure with quantum program optimization, and his background in radio astronomy tools and neural-network-based physics research gives him an unusual cross-disciplinary fluency for applied quantum systems.
7 years of coding experience
5 years of employment as a software developer
Master of Science Physics, Master of Science Physics at ETH ZürichBachelor of Science Physics & Computer Science, Bachelor of Science Physics & Computer Science at McGill University
German, French, English
Github Skills (20)
python10
machine-learning10
html-template10
python-templates10
code-templates10
apptemplates10
quantum-computing10
cpp-templates10
testing9
llvm9
autograd9
automatic-differentiation9
optimization8
cprogramming-language8
c-language8
Programming languages (13)
Github contributions (5)
PennyLaneAI/pennylane
Oct 2021 - Jun 2022
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:
ML Engineer
ML EngineerContributions:483 reviews, 44 commits, 84 PRs in 8 months
Contributions summary:David's commits focus on enhancing the `AmplitudeEmbedding` and `MottonenStatePreparation` templates, key components for quantum machine learning. They implemented parameter batching support for these templates, enabling more efficient processing of multiple sets of input parameters. The user also improved error messages, added testing, and made code simplifications. These changes directly contribute to the functionality and usability of PennyLane for quantum machine learning tasks.
pythonautomatic-differentiationdifferentiable-computingcomputerstensorflow
microsoft/qsharp-compiler
Jun 2021 - Aug 2021
Q# compiler, command line tool, and Q# language server
Role in this project:Backend Developer & Test Automation Engineer
Backend Developer & Test Automation EngineerContributions:14 reviews, 58 commits, 11 PRs in 2 months
Contributions summary:David primarily contributed to the Q# compiler and related tools by updating and fixing existing code, specifically within test templates and the QIR (Quantum Intermediate Representation) optimization example. Their work included refactoring deprecated statements, fixing project names, and adding compilation instructions, including setup for runtime environment on different operating systems. They also added a new example showcasing QIR JIT compilation from Python.
command-line-toolqdkqsharpcompilerquantum-development-kit
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