Gorka Muñoz-Gil

I'm a Marie Skłodowska-Curie postdoctoral fellow at the Quantum Information and Computation group in the Innsbruck University (UIBK) . 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 .

A longer description of what I have been doing during these years can be found in my CV

What you will find here:

A bit of everything! From my current and past research projects, teaching and outreach activities I have been involved with!

Main areas of research

Machine learning and Physics

In recent years, I have been captivated by the potential Machine Learning techniques offer to the study of Physics. We have pioneered the use of such techniques to understand anomalous diffusion and organized the AnDI Challenge , a scientifict competition aimed to further develop the field. Lately, we have studied how artifical learning particles can be used to find optimal foraging strategies (link). I am also interested on how ML techniques apply to the quantum world, from the use of reinforcement learning for the certification of quantum systems (link) to the use of powerful denoising diffusion models for quantum circuit synthesis (link). Last but not least, I also reserach the connection between statistical physics and ML architectures such as Boltzmann machines (link).

Anomalous diffusion: theory and phenomenological models

My PhD Thesis has been devoted to the study of a particular form of motion: anomalous diffusion. We developed models, both for classical and quantum systems, with which to understand which kind of properties, both from the moving particle and the environment, lead to the appearance of such rich phenomena. We are also collaborating with the experimental groups of Prof. Maria García-Parajo (ICFO, link) and Prof. Carlo Manzo (UVic, link), among others, to understand when and how these models arise in biophysical experiments.

Science beyond science

To me, Science does not end after publishing papers. In that sense, I enjoy a lot participating in projects that, while they are often based on my main areas of research, have very different outcomes. For instance, I am currently involved in the Night up project, aimed at the study of light pollution via a citizen science experiment. I was also very lucky to collaborate in the creation of quantum inspired music, which resulted in incredible performance by Reiko Yamada at the Sonar Festival 2021! Moreover, I have participated in plenty of outreach projects both for young students and the general public, check the Teaching tab above to know more!.

selected publications

Book
Modern applications of machine learning in quantum sciences

Dawid, Anna, Arnold, Julian, Requena, Borja, Gresch, Alexander, Płodzień, Marcin, Donatella, Kaelan, Nicoli, Kim, Stornati, Paolo, Koch, Rouven, Büttner, Miriam, Okuła, Robert, Muñoz-Gil, Gorka, [...] Lewenstein, Maciej, and Dauphin, Alexandre

arXiv:2204.04198 2022

Abstract arXiv Bib

In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
@article{dawid2022modern, author = {Dawid, Anna and Arnold, Julian and Requena, Borja and Gresch, Alexander and Płodzień, Marcin and Donatella, Kaelan and Nicoli, Kim and Stornati, Paolo and Koch, Rouven and Büttner, Miriam and Okuła, Robert and Muñoz-Gil, Gorka and [...] Lewenstein, Maciej and Dauphin, Alexandre}, title = {Modern applications of machine learning in quantum sciences}, journal = {arXiv:2204.04198}, bibtex_show = {true}, arxiv = {2204.04198}, year = {2022}, paper_image = {lecture_notes.png}, selected = {true}, abbr = {Book} }
Proc. Natl. Acad. Sci.
Stochastic particle unbinding modulates growth dynamics and size of transcription factor condensates in living cells

Muñoz-Gil, Gorka, Romero, Catalina, Mateos, Nicolas, Llobet Cucalon, Lara Isabel, Filion, Guillaume, Beato, Miguel, Lewenstein, Maciej, Garcı́ia-Parajo, Marı́ia, and Torreno-Pina, Juan

Proceedings of the National Academy of Sciences 2022

Abstract Bib LINK

Living cells organize internal compartments by forming molecular condensates that operate as versatile biochemical “hubs.” Their occurrence is particularly relevant in the nucleus where they regulate, amongst others, gene transcription. However, the biophysics of transcription factor (TF) condensation remains highly unexplored. Through single-molecule experiments in living cells, theory, and simulations, we assessed the diffusion, growth dynamics, and sizes of TF condensates of the nuclear progesterone receptor (PR). Interestingly, PR condensates obey classical growth dynamics at shorter times but deviate at longer times, reaching finite sizes at steady-state. We demonstrate that condensate growth dynamics and nanoscale-size arrested growth is regulated by molecular escaping from condensates, providing an exquisite control of condensate size in nonequilibrium systems such as living cells.
@article{munoz2020phase, title = {Stochastic particle unbinding modulates growth dynamics and size of transcription factor condensates in living cells}, author = {Mu{\~n}oz-Gil, Gorka and Romero, Catalina and Mateos, Nicolas and de Llobet Cucalon, Lara Isabel and Filion, Guillaume and Beato, Miguel and Lewenstein, Maciej and Garc{\'\i}ia-Parajo, Mar{\'\i}ia and Torreno-Pina, Juan}, bibtex_show = {true}, paper_image = {stochastic_unbinding.png}, journal = {Proceedings of the National Academy of Sciences}, abbr = {Proc. Natl. Acad. Sci.}, volume = {119}, number = {31}, pages = {e2200667119}, year = {2022}, publisher = {National Acad. Sciences}, html = {https://doi.org/10.1073/pnas.2200667119}, selected = true }
i.p.a. Nat. Commun.
Quantitative evaluation of methods to analyze motion changes in single-particle experiments (AnDi Challenge 2)

Muñoz-Gil, Gorka, Bachimanchi, Harshith, Pineda, Jesús, Midtvedt, Benjamin, Lewenstein, Maciej, Metzler, Ralf, Krapf, Diego, Volpe, Giovanni, and Manzo, Carlo

In principle accepted in Nature Communications 2023

Abstract arXiv Bib

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 Midtvedt, Benjamin and Lewenstein, Maciej and Metzler, Ralf and Krapf, Diego and Volpe, Giovanni and Manzo, Carlo}, journal = {In principle accepted in Nature Communications}, arxiv = {2311.02041}, paper_image = {andi2_logo.png}, bibtex_show = {true}, year = {2023}, abbr = {i.p.a. Nat. Commun.}, selected = true }