# Research

As you will very quickly see, I am interested in many different topics. If there is a term common to all of them, that is quantum. But after that, I have worked in very fundamental concepts, such as causality (in fact, the main topic of my Ph.D. thesis is quantum networks) or the vacuum, in experiment-motivated fields of thermodynamics, or in more applied topics such as machine learning. I am also genuinely interested in classical machine learning. In fact, with a small group of friends we even attempted to do a Kaggle competition, and scored top 20%! After this, my interest moved closer to unsupervised learning and probabilistic graphical models.

# Publications

Bell inequalities with overlapping measurements

M. Bermejo Morán, A. Pozas-Kerstjens, F. Huber

Proofs of network quantum nonlocality in continuous families of distributions

A. Pozas-Kerstjens, N. Gisin, M.-O. Renou

Physical Review Letters 130, 090201 (2023), arXiv version, code repository

Experimental full network nonlocality with independent sources and strict locality constraints

X.-M. Gu, L. Huang, A. Pozas-Kerstjens, Y.-F. Jiang, D. Wu, B. Bai, Q.-C. Sun, M.-C. Chen, J. Zhang, S. Yu, Q. Zhang, C.-Y. Lu, J.-W. Pan

Certification of non-classicality in all links of a photonic star network without assuming quantum mechanics

N.-N. Wang, A. Pozas-Kerstjens, C. Zhang, B.-H. Liu, Y.-F. Huang, C.-F. Li, G.-C. Guo, N. Gisin, A. Tavakoli

Nature Communications (2023), arXiv version, code repository

Fraud detection with a single-qubit quantum neural network

E. Peña Tapia, G. Scarpa, A. Pozas-Kerstjens

Inflation: a Python library for classical and quantum causal compatibility

E.-C. Boghiu, E. Wolfe, A. Pozas-Kerstjens

Accelerating the training of single-layer binary neural networks using the HHL quantum algorithm

S. Lopez Alarcon, C. Merkel, M. Hoffnagle, S. Ly, A. Pozas-Kerstjens

Proceedings of the IEEE 40th International Conference on Computer Design (ICCD), 427-433 (2022), arXiv version

Single-photon nonlocality in quantum networks

P. Abiuso, T. Kriváchy, E.-C. Boghiu, M.-O. Renou, A. Pozas-Kerstjens, A. Acín

Physics solutions for machine learning privacy leaks

A. Pozas-Kerstjens, S. Hernández-Santana, J. R. Pareja Monturiol, M. Castrillón López, G. Scarpa, C. E. González-Guillén, D. Pérez-García

Full network nonlocality

A. Pozas-Kerstjens, N. Gisin, A. Tavakoli

Physical Review Letters 128, 010403 (2022), arXiv version, code repository

Bell nonlocality in networks

A. Tavakoli, A. Pozas-Kerstjens, M.-X. Luo, M.-O. Renou

Reports on Progress in Physics 85, 056001 (2022), arXiv version

Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines

A. Kehoe, P. Wittek, Y. Xue, A. Pozas-Kerstjens

Machine Learning: Science and Technology 2, 045006 (2021), arXiv version

Genuine Network Multipartite Entanglement

M. Navascués, E. Wolfe, D. Rosset, A. Pozas-Kerstjens

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

A. Pozas-Kerstjens, G. Muñoz-Gil, M. Á. García-March, A. Acín, M. Lewenstein, P. R. Grzybowski

Machine Learning: Science and Technology 2, 025026 (2021), arXiv version, code repository

Quantum inflation: a general approach to quantum causal compatibility

E. Wolfe, A. Pozas-Kerstjens, M. Grinberg, D. Rosset, A. Acín, M. Navascués

Bounding the sets of classical and quantum correlations in networks

A. Pozas-Kerstjens, R. Rabelo, Ł. Rudnicki, R. Chaves, D. Cavalcanti, M. Navascués, A. Acín

Physical Review Letters 123, 140503 (2019), arXiv version, code repository

Bayesian deep learning on a quantum computer

Z. Zhao, A. Pozas-Kerstjens, P. Rebentrost, P. Wittek

Quantum Machine Intelligence 1-2, 41-51 (2019), arXiv version, code repository

A quantum Otto engine with finite heat baths: energy, correlations, and degradation

A. Pozas-Kerstjens, E. G. Brown, K. V. Hovhannisyan

New Journal of Physics 20, 043034 (2018), arXiv version, code repository

Degenerate detectors are unable to harvest spacelike entanglement

A. Pozas-Kerstjens, J. Louko, E. Martín-Martínez

Entanglement harvesting from the electromagnetic vacuum with hydrogenlike atoms

A. Pozas-Kerstjens, E. Martín-Martínez

Harvesting correlations from the quantum vacuum

A. Pozas-Kerstjens, E. Martín-Martínez

# Impact

Our work Proofs of network quantum nonlocality in continuous families of distributions appears featured as Editor’s Suggestion in Physical Review Letters. You can see it on the paper's website.

Our work Bell nonlocality in networks appears featured as Editor’s Recommendation in Reports on Progress in Physics. You can see it on the paper's website.

Our work Efficient training of energy-based models via spin-glass control appears featured as Editor’s Recommendahttps://doi.org/10.1088/1361-6633/ac41bbtion in Machine Learning: Science and Technology. You can see it on the paper's website.

Our work Bayesian deep learning on a quantum computer has been recognized with one of the IBM-Q Best Paper Awards! You can see the press release here.

My colleague Peter Wittek was invited to the Toronto Deep Learning Series to talk about our work Bayesian deep learning on a quantum computer. You can check out his talk here. Also, he did a review of the paper in the AI Socratic Circles blog. This was later converted into a KDnuggets blog story, which earned a Gold medal in July 2019.

The two works Harvesting correlations from the quantum vacuum and Entanglement harvesting from the electromagnetic vacuum with hydrogenlike atoms appear featured in Revista Española de Física 31-1, 40-41 (2017)

# Other resources

Lately I have been teaching some workshops on introductions to quantum computing through Qiskit. The material employed in those workshops can be found in my teaching repository.

ebm-torch is a collection of codes I have developed for playing around with Boltzmann machines in Pytorch. It includes an easy way of defining graphical models, samplers, and optimizers for learning probability distributions from data samples.

For the past years, we have been running in ICFO a reading group focused in classical and quantum machine learning. We stored the topics covered in all sessions, summaries and comments on papers, and even exercises with solutions, in the qml-rg repository.

I instructed an introductory bootcamp to Python and data analysis as part of BIST's Master of Multidisciplinary Research in Experimental Sciences. The content of such bootcamp can be found in this repository.