Giacomo Torlai
Lead Research Scientist, Automation
Los Angeles, California, United States
Summary
👤
Senior🎓
Top SchoolGiacomo Torlai is a Lead Research Scientist in quantum automation with 11 years of experience bridging quantum hardware, tensor networks, and machine learning. He holds a PhD in Physics from the University of Waterloo and has worked across top research institutions and industry labs including Simons Foundation, Caltech, AWS, and Q-CTRL, where he now leads automation research. His work spans both theory and production: developing tensor-network tooling and automatic-differentiation fixes in the widely used ITensors.jl and contributing backend algorithms for many-body quantum ML in NetKet. Giacomo combines deep condensed-matter intuition with practical engineering to ship reproducible quantum state reconstruction and control software. Colleagues value his rare combination of academic rigor and hands-on backend development across Julia and C++ codebases. He is based in Los Angeles and brings a pattern-focused approach to scaling quantum algorithms toward deployable hardware.
11 years of coding experience
8 years of employment as a software developer
Ludwig Maximilian University of MunichBachelor of Science (BSc), Physics, Bachelor of Science (BSc), Physics at University of FlorenceDoctor of Philosophy (PhD), Physics, Doctor of Philosophy (PhD), Physics at University of Waterloo
Italian, English, German
Github Skills (12)
automatic-differentiation10
machine-learning10
c-language10
cprogramming-language10
julia10
test-automation10
linear-algebra9
deep-learning9
neural-network8
artificial-neural-networks8
jax7
python7
Programming languages (4)
Github contributions (5)
netket/netket
Oct 2018 - Mar 2020
Machine learning algorithms for many-body quantum systems
Role in this project:
Back-end Developer
Back-end DeveloperContributions:33 commits, 2 PRs, 3 pushes in 1 year 5 months
Contributions summary:Giacomo implemented dummy functions and loading functions within the `contrastive_divergence.hpp` file, likely as a starting point for handling data and building the core functionality of an unsupervised learning method. Further commits initialized visible layers and implemented gradient calculations. Subsequent commits indicate the user is working towards training with an exact negative phase. The commits relate to implementing the Quantum State Reconstruction method.
markov-chain-monte-carlovariational-monte-carlomonte-carlo-methodsquantum-computingvariational-method
ITensor/ITensors.jl
Nov 2021 - Mar 2022
A Julia library for efficient tensor computations and tensor network calculations. ITensors.jl is supported by the Simons Foundation's Flatiron Institute.
Role in this project:Backend Developer & QA Engineer
Backend Developer & QA EngineerContributions:4 reviews, 5 commits, 6 PRs in 4 months
Contributions summary:Giacomo contributed to the `itensors.jl` library by addressing chain rule implementations for tensor computations, specifically fixing rrules for the `itensor` constructor and related functions. They also enhanced the `op` system with the addition of `+` and `-` algebra, unified `PastaQ.gate` and `ITensors.op`, and added new `OpSum` algebra functions. Furthermore, the user worked on enhancing tests, adding rrules for `apply(U, ::MPO)`, `(::MPO * ::MPO)`, `tr(::MPO)`, and `MPS(Vector{::ITensor})`, which suggests a focus on improving the library's automatic differentiation capabilities and testing its functionality.
matrix-product-statestensor-decompositionautomatic-differentiationtensor-traintensor
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