publications

2024

1. Soft Matter

   Learning how to find targets in the micro-world: the case of intermittent active Brownian particles

   Caraglio, Michele, Kaur, Harpreet, Fiderer, Lukas J, López-Incera, Andrea, Briegel, Hans J, Franosch, Thomas, and Muñoz-Gil, Gorka

   Soft Matter 2024

   Abstract arXiv Bib LINK

   Finding the best strategy to minimize the time needed to find a given target is a crucial task both in nature and in reaching decisive technological advances. By considering learning agents able to switch their dynamics between standard and active Brownian motion, here we focus on developing effective target-search behavioral policies for microswimmers navigating a homogeneous environment and searching for targets of unknown position. We exploit Projective Simulation, a reinforcement learning algorithm, to acquire an efficient stochastic policy represented by the probability of switching the phase, i.e. the navigation mode, in response to the type and the duration of the current phase. Our findings reveal that the target-search efficiency increases with the particle’s self-propulsion during the active phase and that, while the optimal duration of the passive case decreases monotonically with the activity, the optimal duration of the active phase displays a non-monotonic behavior.

   @article{caraglio2024learning,
     title = {Learning how to find targets in the micro-world: the case of intermittent active Brownian particles},
     author = {Caraglio, Michele and Kaur, Harpreet and Fiderer, Lukas J and L{\'o}pez-Incera, Andrea and Briegel, Hans J and Franosch, Thomas and Mu{\~n}oz-Gil, Gorka},
     journal = {Soft Matter},
     year = {2024},
     publisher = {Royal Society of Chemistry},
     arxiv = {https://arxiv.org/abs/2311.16692},
     paper_image = {abm.png},
     bibtex_show = {true},
     abbr = {Soft Matter},
     html = {https://doi.org/10.1039/D3SM01680C}
   }

2. New J. Phys.

   Optimal foraging strategies can be learned

   Muñoz-Gil, Gorka, López-Incera, Andrea, Fiderer, Lukas J, and Briegel, Hans J

   New Journal of Physics 2024

   Abstract arXiv Bib LINK

   The foraging behavior of animals is a paradigm of target search in nature. Understanding which foraging strategies are optimal and how animals learn them are central challenges in modeling animal foraging. While the question of optimality has wide-ranging implications across fields such as economy, physics, and ecology, the question of learnability is a topic of ongoing debate in evolutionary biology. Recognizing the interconnected nature of these challenges, this work addresses them simultaneously by exploring optimal foraging strategies through a reinforcement learning framework. To this end, we model foragers as learning agents. We first prove theoretically that maximizing rewards in our reinforcement learning model is equivalent to optimizing foraging efficiency. We then show with numerical experiments that, in the paradigmatic model of non-destructive search, our agents learn foraging strategies which outperform the efficiency of some of the best known strategies such as Lévy walks. These findings highlight the potential of reinforcement learning as a versatile framework not only for optimizing search strategies but also to model the learning process, thus shedding light on the role of learning in natural optimization processes.

   @article{munoz2023optimal,
     title = {Optimal foraging strategies can be learned},
     author = {Mu{\~n}oz-Gil, Gorka and L{\'o}pez-Incera, Andrea and Fiderer, Lukas J and Briegel, Hans J},
     journal = {New Journal of Physics},
     arxiv = {2303.06050},
     volume = {26},
     number = {1},
     pages = {013010},
     year = {2024},
     html = {https://doi.org/10.1088/1367-2630/ad19a8},
     abbr = {New J. Phys.},
     paper_image = {rl_opts.png},
     bibtex_show = {true}
   }

2023

1. 

   Quantum circuit synthesis with diffusion models

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

   arXiv:2311.02041 2023

   Abstract arXiv Bib

   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 = {arXiv:2311.02041},
     year = {2023},
     arxiv = {2311.02041},
     paper_image = {genqc.png},
     bibtex_show = {true}
   }

2. 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
   }

3. Biophys. J.

   Inferring pointwise diffusion properties of single trajectories with deep learning

   Requena, Borja, Masó-Orriols, Sergi, Bertran, Joan, Lewenstein, Maciej, Manzo, Carlo, and Muñoz-Gil, Gorka

   Biophysical Journal 2023

   Abstract arXiv Bib LINK

   To characterize the mechanisms governing the diffusion of particles in biological scenarios, it is essential to accurately determine their diffusive properties. To do so, we propose a machine-learning method to characterize diffusion processes with time-dependent properties at the experimental time resolution. Our approach operates at the single-trajectory level predicting the properties of interest, such as the diffusion coefficient or the anomalous diffusion exponent, at every time step of the trajectory. In this way, changes in the diffusive properties occurring along the trajectory emerge naturally in the prediction and thus allow the characterization without any prior knowledge or assumption about the system. We first benchmark the method on synthetic trajectories simulated under several conditions. We show that our approach can successfully characterize both abrupt and continuous changes in the diffusion coefficient or the anomalous diffusion exponent. Finally, we leverage the method to analyze experiments of single-molecule diffusion of two membrane proteins in living cells: the pathogen-recognition receptor DC-SIGN and the integrin α5β1. The analysis allows us to characterize physical parameters and diffusive states with unprecedented accuracy, shedding new light on the underlying mechanisms.

   @article{REQUENA2023,
     title = {Inferring pointwise diffusion properties of single trajectories with deep learning},
     journal = {Biophysical Journal},
     year = {2023},
     month = oct,
     issn = {0006-3495},
     doi = {https://doi.org/10.1016/j.bpj.2023.10.015},
     html = {https://www.sciencedirect.com/science/article/pii/S0006349523006513},
     author = {Requena, Borja and Masó-Orriols, Sergi and Bertran, Joan and Lewenstein, Maciej and Manzo, Carlo and Muñoz-Gil, Gorka},
     paper_image = {step.png},
     bibtex_show = {true},
     arxiv = {arXiv:2302.00410},
     abbr = {Biophys. J.}
   }

4. 

   Learning minimal representations of stochastic processes with variational autoencoders

   Fernández-Fernández, Gabriel, Manzo, Carlo, Lewenstein, Maciej, Dauphin, Alexandre, and Muñoz-Gil, Gorka

   arXiv:2307.11608 2023

   Abstract arXiv Bib

   Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertainty, they are however difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic process. Our method builds upon an extended β-variational autoencoder architecture. By means of simulated datasets corresponding to paradigmatic diffusion models, we showcase its effectiveness in extracting the minimal relevant parameters that accurately describe these dynamics. Furthermore, the method enables the generation of new trajectories that faithfully replicate the expected stochastic behavior. Overall, our approach enables for the autonomous discovery of unknown parameters describing stochastic processes, hence enhancing our comprehension of complex phenomena across various fields.

   @article{fernandez2023learning,
     title = {Learning minimal representations of stochastic processes with variational autoencoders},
     author = {Fern{\'a}ndez-Fern{\'a}ndez, Gabriel and Manzo, Carlo and Lewenstein, Maciej and Dauphin, Alexandre and Mu{\~n}oz-Gil, Gorka},
     journal = {arXiv:2307.11608},
     year = {2023},
     arxiv = {2307.11608},
     paper_image = {bvae.png},
     bibtex_show = {true}
   }

5. Phys. Rev. Res.

   Certificates of quantum many-body properties assisted by machine learning

   Requena, Borja, Muñoz-Gil, Gorka, Lewenstein, Maciej, Dunjko, Vedran, and Tura, Jordi

   Phys. Rev. Res. 2023

   Abstract arXiv Bib

   Computationally intractable tasks are often encountered in physics and optimization. Such tasks often comprise a cost function to be optimized over a so-called feasible set, which is specified by a set of constraints. This may yield, in general, to difficult and non-convex optimization tasks. A number of standard methods are used to tackle such problems: variational approaches focus on parameterizing a subclass of solutions within the feasible set; in contrast, relaxation techniques have been proposed to approximate it from outside, thus complementing the variational approach by providing ultimate bounds to the global optimal solution. In this work, we propose a novel approach combining the power of relaxation techniques with deep reinforcement learning in order to find the best possible bounds within a limited computational budget. We illustrate the viability of the method in the context of finding the ground state energy of many-body quantum systems, a paradigmatic problem in quantum physics. We benchmark our approach against other classical optimization algorithms such as breadth-first search or Monte-Carlo, and we characterize the effect of transfer learning. We find the latter may be indicative of phase transitions, with a completely autonomous approach. Finally, we provide tools to generalize the approach to other common applications in the field of quantum information processing.

   @article{requena2021certificates,
     title = {Certificates of quantum many-body properties assisted by machine learning},
     author = {Requena, Borja and Mu{\~n}oz-Gil, Gorka and Lewenstein, Maciej and Dunjko, Vedran and Tura, Jordi},
     journal = {Phys. Rev. Res.},
     volume = {5},
     issue = {1},
     pages = {013097},
     numpages = {26},
     year = {2023},
     month = feb,
     publisher = {American Physical Society},
     doi = {10.1103/PhysRevResearch.5.013097},
     url = {https://link.aps.org/doi/10.1103/PhysRevResearch.5.013097},
     bibtex_show = {true},
     arxiv = {2103.03830},
     paper_image = {certificates.png},
     abbr = {Phys. Rev. Res.}
   }

6. J. Phys. A

   Preface: characterisation of physical processes from anomalous diffusion data

   Manzo, Carlo, Muñoz-Gil, Gorka, Volpe, Giovanni, Garcia-March, Miguel Angel, Lewenstein, Maciej, and Metzler, Ralf

   Journal of Physics A: Mathematical and Theoretical 2023

   arXiv Bib LINK

   @article{Manzo_2023,
     doi = {10.1088/1751-8121/acb1e1},
     html = {https://dx.doi.org/10.1088/1751-8121/acb1e1},
     year = {2023},
     month = jan,
     publisher = {IOP Publishing},
     volume = {56},
     number = {1},
     pages = {010401},
     author = {Manzo, Carlo and Muñoz-Gil, Gorka and Volpe, Giovanni and Garcia-March, Miguel Angel and Lewenstein, Maciej and Metzler, Ralf},
     title = {Preface: characterisation of physical processes from anomalous diffusion data},
     journal = {Journal of Physics A: Mathematical and Theoretical},
     arxiv = {https://arxiv.org/abs/2301.00800},
     paper_image = {rw_warping.png},
     bibtex_show = {true},
     abbr = {J. Phys. A}
   }

2022

1. 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}
   }

2. 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
   }

3. Remote Sens.

   Citizen Science to Assess Light Pollution with Mobile Phones

   Muñoz-Gil, Gorka, Dauphin, Alexandre, Beduini, Federica A, and Miguel, Alejandro

   Remote Sensing 2022

   Abstract arXiv Bib LINK

   The analysis of the colour of artificial lights at night has an impact on diverse fields, but current data sources have either limited resolution or scarce availability of images for a specific region. In this work, we propose crowdsourced photos of streetlights as an alternative data source: for this, we designed NightUp Castelldefels, a pilot for a citizen science experiment aimed at collecting data about the colour of streetlights. In particular, we extract the colour from the collected images and compare it to an official database, showing that it is possible to classify streetlights according to their colour from photos taken by untrained citizens with their own smartphones. We also compare our findings to the results obtained from one of the current sources for this kind of study. The comparison highlights how the two approaches give complementary information about artificial lights at night in the area. This work opens a new avenue in the study of the colour of artificial lights at night with the possibility of accurate, massive and cheap data collection.

   @article{munoz2022citizen,
     title = {Citizen Science to Assess Light Pollution with Mobile Phones},
     author = {Mu{\~n}oz-Gil, Gorka and Dauphin, Alexandre and Beduini, Federica A and S{\'a}nchez de Miguel, Alejandro},
     journal = {Remote Sensing},
     abbr = {Remote Sens.},
     volume = {14},
     number = {19},
     pages = {4976},
     year = {2022},
     publisher = {MDPI},
     arxiv = {2210.11140},
     bibtex_show = {true},
     paper_image = {nightup.jpg},
     html = {https://doi.org/10.3390/rs14194976}
   }

2021

1. Nat. Commun.

   Objective comparison of methods to decode anomalous diffusion

   Muñoz-Gil, Gorka, Volpe, Giovanni, Garcia-March, Miguel Angel [...], and Manzo, Carlo

   Nature Communications 2021

   Abstract arXiv Bib LINK

   Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics, playing a crucial role in phenomena from quantum physics to life sciences. The detection and characterization of anomalous diffusion from the measurement of an individual trajectory are challenging tasks, which traditionally rely on calculating the mean squared displacement of the trajectory. However, this approach breaks down for cases of important practical interest, e.g., short or noisy trajectories, ensembles of heterogeneous trajectories, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams independently applied their own algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, providing practical advice for users and a benchmark for developers.

   @article{munoz2021objective,
     title = {Objective comparison of methods to decode anomalous diffusion},
     author = {Mu{\~n}oz-Gil, Gorka and Volpe, Giovanni and Garcia-March, Miguel Angel [...] and Manzo, Carlo},
     journal = {Nature Communications},
     year = {2021},
     month = oct,
     day = {29},
     volume = {12},
     number = {1},
     pages = {6253},
     issn = {2041-1723},
     doi = {10.1038/s41467-021-26320-w},
     html = {https://doi.org/10.1038/s41467-021-26320-w},
     bibtex_show = {true},
     arxiv = {2105.06766},
     abbr = {Nat. Commun.},
     paper_image = {logo.png}
   }

2. ML: sci. technol.

   Efficient training of energy-based models via spin-glass control

   Pozas-Kerstjens, Alejandro, Muñoz-Gil, Gorka, Piñol, Eloy, Garcı́a-March, Miguel Ángel, Acı́n, Antonio, Lewenstein, Maciej, and Grzybowski, Przemysław R

   Machine Learning: Science and Technology 2021

   Abstract arXiv Bib LINK

   We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the weights of Boltzmann machines. We use it to learn the Bars and Stripes dataset of various sizes and the MNIST dataset, and show how they quickly achieve the performance offered by standard methods for unsupervised learning. Our results indicate that the standard initialization of Boltzmann machines with random weights equivalent to spin-glass models is an unnecessary bottleneck in the process of training. Furthermore, this new family allows for very easy access to low-energy configurations, which points to new, efficient training algorithms. The simplest variant of such algorithms approximates the negative phase of the log-likelihood gradient with no Markov chain Monte Carlo sampling costs at all, and with an accuracy sufficient to achieve good learning and generalization.

   @article{pozas2021efficient,
     title = {Efficient training of energy-based models via spin-glass control},
     author = {Pozas-Kerstjens, Alejandro and Mu{\~n}oz-Gil, Gorka and Pi{\~n}ol, Eloy and Garc{\'\i}a-March, Miguel {\'A}ngel and Ac{\'\i}n, Antonio and Lewenstein, Maciej and Grzybowski, Przemys{\l}aw R},
     journal = {Machine Learning: Science and Technology},
     volume = {2},
     number = {2},
     pages = {025026},
     year = {2021},
     publisher = {IOP Publishing},
     abbr = {ML: sci. technol.},
     bibtex_show = {true},
     arxiv = {1910.01592},
     html = {https://iopscience.iop.org/article/10.1088/2632-2153/abe807/meta},
     paper_image = {rbm.png}
   }

3. J. Phys. A

   Unsupervised learning of anomalous diffusion data: an anomaly detection approach

   Muñoz-Gil, Gorka, Corominas, Guillem Guigo, and Lewenstein, Maciej

   Journal of Physics A: Mathematical and Theoretical 2021

   Abstract arXiv Bib LINK

   The characterization of diffusion processes is a keystone in our understanding of a variety of physical phenomena. Many of these deviate from Brownian motion, giving rise to anomalous diffusion. Various theoretical models exists nowadays to describe such processes, but their application to experimental setups is often challenging, due to the stochastic nature of the phenomena and the difficulty to harness reliable data. The latter often consists on short and noisy trajectories, which are hard to characterize with usual statistical approaches. In recent years, we have witnessed an impressive effort to bridge theory and experiments by means of supervised machine learning techniques, with astonishing results. In this work, we explore the use of unsupervised methods in anomalous diffusion data. We show that the main diffusion characteristics can be learnt without the need of any labelling of the data. We use such method to discriminate between anomalous diffusion models and extract their physical parameters. Moreover, we explore the feasibility of finding novel types of diffusion, in this case represented by compositions of existing diffusion models. At last, we showcase the use of the method in experimental data and demonstrate its advantages for cases where supervised learning is not applicable.

   @article{munoz2021unsupervised,
     doi = {10.1088/1751-8121/ac3786},
     year = {2021},
     month = nov,
     publisher = {IOP Publishing},
     volume = {54},
     number = {50},
     pages = {504001},
     author = {Mu{\~{n}}oz-Gil, Gorka and i Corominas, Guillem Guigo and Lewenstein, Maciej},
     title = {Unsupervised learning of anomalous diffusion data: an anomaly detection approach},
     journal = {Journal of Physics A: Mathematical and Theoretical},
     html = {https://iopscience.iop.org/article/10.1088/1751-8121/ac3786/meta},
     abbr = {J. Phys. A},
     paper_image = {unsupervised.png},
     arxiv = {2108.03411},
     bibtex_show = {true}
   }

2020

1. New J. Phys. NJP Best 2020

   Single trajectory characterization via machine learning

   Muñoz-Gil, Gorka, Garcia-March, Miguel Angel, Manzo, Carlo, Martı́n-Guerrero, José D, and Lewenstein, Maciej

   New Journal of Physics 2020

   Abstract arXiv Bib LINK

   In order to study transport in complex environments, it is extremely important to determine the physical mechanism underlying diffusion, and precisely characterize its nature and parameters. Often, this task is strongly impacted by data consisting of trajectories with short length and limited localization precision. In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate even very short trajectories to the underlying diffusion mechanism with a high accuracy. In addition, the method is able to classify the motion according to normal or anomalous diffusion, and determine its anomalous exponent with a small error. The method provides highly accurate outputs even when working with very short trajectories and in the presence of experimental noise. We further demonstrate the application of transfer learning to experimental and simulated data not included in the training/testing dataset. This allows for a full, high-accuracy characterization of experimental trajectories without the need of any prior information.

   @article{munoz2020single,
     title = {Single trajectory characterization via machine learning       },
     author = {Mu{\~n}oz-Gil, Gorka and Garcia-March, Miguel Angel and Manzo, Carlo and Mart{\'\i}n-Guerrero, Jos{\'e} D and Lewenstein, Maciej},
     journal = {New Journal of Physics},
     tag_name = {NJP Best 2020},
     tag_url = {https://icfo.eu/newsroom/news/5053-njp-s-best-of-2020},
     volume = {22},
     number = {1},
     pages = {013010},
     year = {2020},
     publisher = {IOP Publishing},
     abbr = {New J. Phys.},
     bibtex_show = {true},
     arxiv = {1903.02850},
     html = {https://iopscience.iop.org/article/10.1088/1367-2630/ab6065/meta},
     paper_image = {mltraj.png}
   }

2. Quantum

   Control of anomalous diffusion of a Bose polaron

   Charalambous, Christos, Garcı́a-March, Miguel Ángel, Muñoz-Gil, Gorka, Grzybowski, Przemysław Ryszard, and Lewenstein, Maciej

   Quantum 2020

   Abstract arXiv Bib LINK

   We study the diffusive behavior of a Bose polaron immersed in a coherently coupled two-component Bose-Einstein Condensate (BEC). Polaron superdiffuses if it couples in the same manner to both components, i.e. either attractively or repulsively to both of them. This is the same behavior as that of an impurity immersed in a single BEC. Conversely, the polaron exhibits a transient nontrivial subdiffusive behavior if it couples attractively to one of the components and repulsively to the other. The anomalous diffusion exponent and the duration of the subdiffusive interval can be controlled with the Rabi frequency of the coherent coupling between the two components, and with the coupling strength of the impurity to the BEC.

   @article{charalambous2020control,
     title = {Control of anomalous diffusion of a Bose polaron},
     author = {Charalambous, Christos and Garc{\'\i}a-March, Miguel {\'A}ngel and Mu{\~n}oz-Gil, Gorka and Grzybowski, Przemys{\l}aw Ryszard and Lewenstein, Maciej},
     journal = {Quantum},
     volume = {4},
     pages = {232},
     year = {2020},
     publisher = {Verein zur F{\"o}rderung des Open Access Publizierens in den Quantenwissenschaften},
     abbr = {Quantum},
     bibtex_show = {true},
     arxiv = {1910.01571},
     html = {https://quantum-journal.org/papers/q-2020-02-20-232/},
     paper_image = {qbrownian.jpg}
   }

2019

1. Front. Phys

   Diffusion through a network of compartments separated by partially-transmitting boundaries

   Muñoz-Gil, Gorka, Garcia-March, Miguel Angel, Manzo, Carlo, Celi, Alessio, and Lewenstein, Maciej

   Frontiers in Physics 2019

   Abstract arXiv Bib LINK

   We study the random walk of a particle in a compartmentalized environment, as realized in biological samples or solid state compounds. Each compartment is characterized by its length L and the boundaries transmittance T. We identify two relevant spatio-temporal scales that provide alternative descriptions of the dynamics: i) the microscale, in which the particle position is monitored at constant time intervals; and ii) the mesoscale, in which it is monitored only when the particle crosses a boundary between compartments. Both descriptions provide –by construction– the same long time behavior. The analytical description obtained at the proposed mesoscale allows for a complete characterization of the complex movement at the microscale, thus representing a fruitful approach for this kind of systems. We show that the presence of disorder in the transmittances is a necessary condition to induce anomalous diffusion, whereas the spatial heterogeneity reduces the degree of subdiffusion and, in some cases, can even compensate for the disorder induced by the stochastic transmittance.

   @article{munoz2019diffusion,
     title = {Diffusion through a network of compartments separated by partially-transmitting boundaries},
     author = {Mu{\~n}oz-Gil, Gorka and Garcia-March, Miguel Angel and Manzo, Carlo and Celi, Alessio and Lewenstein, Maciej},
     journal = {Frontiers in Physics},
     volume = {7},
     pages = {31},
     year = {2019},
     publisher = {Frontiers},
     abbr = {Front. Phys},
     bibtex_show = {true},
     arxiv = {1810.02096},
     html = {https://www.frontiersin.org/articles/10.3389/fphy.2019.00031/full},
     paper_image = {comp.png}
   }

2017

1. Phys. Rev. E

   Nonergodic subdiffusion from transient interactions with heterogeneous partners

   Charalambous, C, Muñoz-Gil, G, Celi, A, Garcia-Parajo, MF, Lewenstein, M, Manzo, C, and Garcı́a-March, MA

   Physical Review E 2017

   Abstract arXiv Bib LINK

   Spatiotemporal disorder has been recently associated to the occurrence of anomalous nonergodic diffusion of molecular components in biological systems, but the underlying microscopic mechanism is still unclear. We introduce a model in which a particle performs continuous Brownian motion with changes of diffusion coefficients induced by transient molecular interactions with diffusive binding partners. In spite of the exponential distribution of waiting times, the model shows subdiffusion and nonergodicity similar to the heavy-tailed continuous time random walk. The dependence of these properties on the density of binding partners is analyzed and discussed. Our work provide an experimentally-testable microscopic model to investigate the nature of nonergodicity in disordered media.

   @article{charalambous2017nonergodic,
     title = {Nonergodic subdiffusion from transient interactions with heterogeneous partners},
     author = {Charalambous, C and Mu{\~n}oz-Gil, G and Celi, A and Garcia-Parajo, MF and Lewenstein, M and Manzo, C and Garc{\'\i}a-March, MA},
     journal = {Physical Review E},
     volume = {95},
     number = {3},
     pages = {032403},
     year = {2017},
     publisher = {APS},
     abbr = {Phys. Rev. E},
     bibtex_show = {true},
     arxiv = {1607.00189},
     html = {https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.032403},
     paper_image = {hunters.png}
   }

2. Phys. Rev. E

   Transient subdiffusion from an Ising environment

   Muñoz-Gil, G, Charalambous, C, Garcı́a-March, MA, Garcia-Parajo, MF, Manzo, C, Lewenstein, M, and Celi, A

   Physical Review E 2017

   Abstract arXiv Bib LINK

   We introduce a model, in which a particle performs a continuous time random walk (CTRW) coupled to an environment with Ising dynamics. The particle shows locally varying diffusivity determined by the geometrical properties of the underlying Ising environment, that is, the diffusivity depends on the size of the connected area of spins pointing in the same direction. The model shows anomalous diffusion when the Ising environment is at critical temperature. We show that any finite scale introduced by a temperature different from the critical one, or a finite size of the environment, cause subdiffusion only during a transient time. The characteristic time, at which the system returns to normal diffusion after the subdiffusive plateau depends on the limiting scale and on how close the temperature is to criticality. The system also displays apparent ergodicity breaking at intermediate time, while ergodicity breaking at longer time occurs only under the idealized infinite environment at the critical temperature.

   @article{munoz2017transient,
     title = {Transient subdiffusion from an Ising environment},
     author = {Mu{\~n}oz-Gil, G and Charalambous, C and Garc{\'\i}a-March, MA and Garcia-Parajo, MF and Manzo, C and Lewenstein, M and Celi, A},
     journal = {Physical Review E},
     volume = {96},
     number = {5},
     pages = {052140},
     year = {2017},
     publisher = {APS},
     abbr = {Phys. Rev. E},
     bibtex_show = {true},
     arxiv = {1708.01019},
     html = {https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.052140},
     paper_image = {ising.png}
   }