Gorka Muñoz-Gil

I'm a Senior Postdoctoral Researcher in the Quantum Information and Computation group at the University of Innsbruck (UIBK), Austria.

I got my PhD in the Quantum Optics theory group at ICFO ( November 2020! ). Before that I got a MSc in Photonics at the Universitat Politècnica de Catalunya and BSc in Physics at the Universitat Autònoma de Barcelona .

You can find a detailed overview of my career in my CV

Main areas of research

My research focuses on developing and applying machine learning methods in physics, with a special emphasis on interpretable approaches.

ML in the quantum domain

In recent years, my main focus has been using ML to push the boundaries of quantum science. One research line explores generative AI, in particular diffusion denoising models, for generating quantum circuits. Our proposal was featured on the cover of Nature Machine Intelligence. We are now expanding this work on several fronts, especially through a collaboration with Nvidia, leveraging their new CUDA-Q library and hardware (see our new preprint).

Another key direction of my work aims to develop interpretable ML approaches for discovering new quantum phenomena. We recently demonstrated how variational autoencoders can uncover the phase structure of experimental quantum simulators—independently of their implementation—from Rydberg atom arrays in Prof. Lukin’s group at Harvard to Bose–Einstein condensates in Prof. Schmiedmayer’s group in Vienna.

From soft to active matter: a ride through biophysics

Anomalous diffusion was the focus of my PhD thesis. In collaboration with the biophysics groups of Prof. Manzo (Vic) and Prof. Volpe (Gothenburg), we pioneered the use of ML to characterize diffusion trajectories through the AnDi Challenge, a global scientific competition with over 150 participants across two editions. The results of the latest challenge were recently published in Nature Communications.

More recently, I have explored optimal search strategies using interpretable reinforcement learning, gaining theoretical insights applicable to the motion of intelligent systems across soft matter and ecology (see paper, and others in the Publications tab).

Science beyond science

Science doesn’t end with publications. I enjoy engaging in projects that extend my involvement in science beyond the lab—from teaching to outreach. I currently teach the Machine Learning in Physics Master course and have led a summer camp on quantum information for high school students (see Teaching tab).

I am also committed to open science and outreach, from citizen-science initiatives such as NightUp! to public talks at Pint of Science and artistic collaborations—like a quantum-inspired music piece performed by Reiko Yamada at the Sonar Festival 2021.

News

Sep 2025	New pre-print: Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator ! In collaboration with Prof. Schmiedmayer group, we demonstrate the ability of interpetable AI to extract the physical descriptors of a quantum system, without any supervision.
Jul 2025	New publication: the 2nd AnDi Challenge paper has just been published in Nature Communications. This paper gathers all the details about the challenge as well as the impressive results reached by the participants!
Jun 2025	New pre-print: Synthesis of discrete-continuous quantum circuits with multimodal diffusion models! We’re back with quantum circuit synthesis using diffusion models, this time in collaboration with NVIDIA, making use of their new CUDA-Q library and hardware.

Recent selected publications

Nat. Mach. Intell.
Quantum circuit synthesis with diffusion models

Fürrutter, Florian, Muñoz-Gil, Gorka, and Briegel, Hans J

Nature Machine Intelligence 2024

Abstract arXiv Bib LINK

Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. In this work, we use generative machine learning models, specifically denoising diffusion models (DMs), to facilitate this transformation. Leveraging text-conditioning, we steer the model to produce desired quantum operations within gate-based quantum circuits. Notably, DMs allow to sidestep during training the exponential overhead inherent in the classical simulation of quantum dynamics – a consistent bottleneck in preceding ML techniques. We demonstrate the model’s capabilities across two tasks: entanglement generation and unitary compilation. The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device. Given their flexibility and generalization abilities, we envision DMs as pivotal in quantum circuit synthesis, enhancing both practical applications but also insights into theoretical quantum computation.
@article{furrutter2023quantum, title = {Quantum circuit synthesis with diffusion models}, author = {F{\"u}rrutter, Florian and Mu{\~n}oz-Gil, Gorka and Briegel, Hans J}, journal = {Nature Machine Intelligence}, pages = {1--10}, year = {2024}, arxiv = {2311.02041}, paper_image = {gen_qc.png}, bibtex_show = {true}, abbr = {Nat. Mach. Intell.}, html = {https://doi.org/10.1038/s42256-024-00831-9}, selected = {true} }
Nat. Commun.
Quantitative evaluation of methods to analyze motion changes in single-particle experiments (AnDi Challenge 2)

Muñoz-Gil, Gorka, Bachimanchi, Harshith, Pineda, Jesús [...], Volpe, Giovanni, and Manzo, Carlo

Nature Communications 2025

Abstract arXiv Bib LINK

The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity of transport processes and interactions between cell components. These characteristics are seen as motion changes in the particle trajectories. Despite the existence of multiple approaches to carry out this type of analysis, no objective assessment of these methods has been performed so far. Here, we have designed a competition to characterize and rank the performance of these methods when analyzing the dynamic behavior of single molecules. To run this competition, we have implemented a software library to simulate realistic data corresponding to widespread diffusion and interaction models, both in the form of trajectories and videos obtained in typical experimental conditions. The competition will constitute the first assessment of these methods, provide insights into the current limits of the field, foster the development of new approaches, and guide researchers to identify optimal tools for analyzing their experiments.
@article{munoz2023quantitative, title = {Quantitative evaluation of methods to analyze motion changes in single-particle experiments (AnDi Challenge 2)}, author = {Mu{\~n}oz-Gil, Gorka and Bachimanchi, Harshith and Pineda, Jes{\'u}s [...] and Volpe, Giovanni and Manzo, Carlo}, journal = {Nature Communications}, arxiv = {2311.02041}, paper_image = {andi2_logo.png}, bibtex_show = {true}, year = {2025}, html = {https://doi.org/10.1038/s41467-025-61949-x}, abbr = {Nat. Commun.}, selected = {true} }