Alexander Del Toro Barba, PhD

Alexander is senior machine learning engineer and quantum computing practice lead at Google Cloud. After earning his PhD in 2018, he joined Google Cloud as AI specialist, where he collaborates with research, engineering, and product teams to support customers in building scalable machine learning solutions.

In 2020, Alexander became practice lead for AI/ML to develop a large community of ML engineers. Since 2022, he built up the quantum computing practice at Google Cloud. Alexander contributed to DeepMind research in 2020 and to Quantum AI research on quantum algorithms since 2022.

RESEARCH

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quantum algorithms • quantum machine learning

My passion is quantum machine learning and natural sciences, particularly astrophysics. My long-term goal is to advance astrophysical research with quantum machine learning, such as detecting life on exoplanets with super-resolution quantum imaging. See my Github repo ➡

PUBLICATIONS

American Physical Society

Analyzing Prospects for Quantum Advantage in Topological Data Analysis ,

PRX Quantum 5 (1), 010319 (2024)*

Science

Dynamics of magnetization at infinite temperature in a Heisenberg spin chain

Science 384, 48-53 (2024)

Stable quantum-correlated many-body states through engineered dissipation

Science 383, 1332-1337 (2024)

Nature

Thermalization and criticality on an analogue–digital quantum simulator

Nature 638, 79-85 (2025)

Phase transitions in Random Circuit Sampling

Nature 634, 328-333 (2024)

Measurement-induced entanglement and teleportation on a noisy quantum processor

Nature 622, 481–486 (2023)

Non-Abelian braiding of graph vertices in a superconducting processor

Nature 618, 264–269 (2023)

Suppressing quantum errors by scaling a surface code logical qubit

Nature 614, 676–681 (2023)

Formation of robust bound states of interacting microwave photons

Nature 612, 240–245 (2022)

arXiv

Demonstrating Dynamic Surface Codes

arXiv: 2412.14360 (2024)

Observation of disorder-free localization and efficient disorder averaging on a quantum processor

arXiv:2410.06557 (2024)

This publication list from Google Scholar include Google Quantum AI research milestone papers as team collaborator, as well as focus papers with direct contributions as an author*

GOOGLE

As senior machine learning engineer, Alexander collaborates with Google research, Cloud AI engineering, and product teams to support Google Cloud customers in building innovate and scalable machine learning solutions, covering both technical and business aspects:

Alexander supports technically on training, tuning, evaluation, serving and observability of LLMs (Gemini and 3P like Llama) for building multi-agent and multi-model platforms, as well as traditional, predictive ML models. Alexander advises customers on their overall machine learning roadmap and on specific MLOps topics.
Alexander supports management in triaging AI/ML expert requests prioritized by technical feasibility and business impact. Alexander also built up a large community of ML engineers for knowledge sharing and mutual support on customer engagements.
Alexander leads conversations with C-executives during Executive Business Councils, and runs presentations at public events about trends, insights and learnings about AI/ML.

Additionally, Alexander built up and leads the quantum computing practice at Google Cloud in collaboration with Google Cloud HPC leadership and Google Quantum AI research:

Build up a community of approved quantum speakers globally. Continuous knowledge sharing across Google Cloud. Covering and triaging Google Cloud customer requests about quantum computing and quantum-inspired HPC computing globally.
Contributions: Keynote about Quantum Machine Learning at ML Prague conference (2924), a podcast about quantum computing on Spotify, Google or Apple in 2023, and a presentation about quantum computing at the 'Conference on Quantum Technologies for High-Energy Physics' at CERN in 2022.

Alexander also supports Google research on advancing quantum machine learning algorithms, such as for Quantum Topological Data Analysis and classical limits of information gain with quantum data. He also contributed to DeepMind applied research in grid intelligence for wind power optimization with machine learning.