Elies Gil-Fuster

emgilfuster <at] gmail (dot} com

I'm a last-year PhD student working on quantum computing. I have the privilege of being advised by Jens Eisert and Vedran Dunjko.

During my PhD, I have studied foundational aspects of quantum machine learning models, including generalization, expressivity, and the potential for quantum advantage.

Beyond learning, I’m interested in quantum complexity theory, quantum algorithmic techniques, and query complexity.

Concretely, I’m excited to work on theory questions that capture real-life scenarios.

I'm currently searching for postdoc positions starting Fall 2027.

News

2026 / 10

We posted "A provable quantum advantage for approximate optimization via decoded quantum interferometry" on arxiv.

2026 / 09

Our work "Optimal algorithmic complexity of inference in quantum kernel methods" was accepted as a poster in NeurIPS 2026! See you in Paris :)
I was selected for the Foundations of Artificial Intelligence Rising Stars Workshop at University of Warwick, where I will give a talk.
I gave a contributed talk on "Optimal algorithmic complexity of inference in quantum kernel methods" at a QuSoft workshop on Quantum Algorithms.
I was invited to co-organize the 2nd AIMS Workshop on the Theory of Quantum Learning Algorithms, which takes place in Cape Town.

2026 / 07

I was invited to give a talk on "Optimal algorithmic complexity of inference in quantum kernel methods" online in the Quantum Seminar Series at HSBC.
We posted "Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods" on arxiv.
We posted "Cautious optimism for deep parameterized quantum circuits" on arxiv.

2026 / 06

I was invited to give a talk on "Optimal algorithmic complexity of inference in quantum kernel methods" online in the QSL Seminar at University of Edinburgh.
I was invited to visit Thomas Vidick's group at EPFL, where I gave a talk on "Approximate classical sampling for DQI via MCMC". I also gave a tutorial on "Decoded Quantum Interferometry" in Zoe Holmes' group.

2026 / 05

I was invited to visit the TPIII group at JMU Würzburg, where I gave a talk on "The quest for good QML models".

2026 / 04

I attended the second Quantum Cambridge Oxford Warwick (QCOW) Colloquium on Quantum Learning Theory in University of Warwick, where I gave a talk on "The quest for good QML models".
I attended the conference Quantum Computing Theory in Practice (QCTiP 2026) in Oxford, where I presented a poster on "Prospects for quantum advantage in supervised learning from the representability of functions".
I was invited to give a talk on "Prospects for quantum advantage in supervised learning from the representability of functions" in the Qunipa Seminar at University of Palermo.
We posted "Optimal algorithmic complexity of inference in quantum kernel methods" on arxiv.
I co-designed a workshop on "True randomness from quantum computers" for Girls'Day at Freie Universität Berlin.

2026 / 03

I was invited to the program committee of the IEEE International Conference on Quantum Computing and Engineering (QCE26), which takes place in Toronto.
We posted "A PAC-Bayesian approach to generalization for quantum models" on arxiv.

2026 / 02

I gave a tutorial on "Decoded Quantum Interferometry" in our group's tutorial series, in Berlin.

2026 / 01

Our work "Double descent in Quantum Kernel Methods" was published in PRX Quantum!
I was invited to give a talk on "Potential for quantum advantage in supervised learning with quantum learning models" online in the IBM Quantum (US) Seminar.

2025 / 12

I organized a pubquiz for our group's QMIO Holiday Special 2025!
We posted "Prospects for quantum advantage in machine learning from the representability of functions" on arxiv.

2025 / 11

I attended the conference Quantum Techniques in Machine Learning (QTML2025) in Singapore, where I gave a talk on "Kernel-based dequantization of variational QML". My collaborator Sergi Masot-Llima presented a poster on our work "Prospects for quantum advantage in supervised learning from the representability of functions".

2025 / 10

I attended the 1st AIMS Workshop on the Theory of Quantum Learning Algorithms in the African Institute for Mathematical Sciences (AIMS) in Cape Town.
I moderated two events within the Sensing Quantum Symposium by LAS Art Foundation, including a panel with Hartmut Neven and Elham Kashefi on "Quantum x AI: shaping what's next", at Silent Green in Berlin.
I started a two-month research visit in Vedran Dunjko's group, at Leiden University, as part of my Ellis PhD program.

2025 / 07

I was invited to visit Marcello Dalmonte's group at ICTP in Trieste, where I gave a talk on "On the relation between trainability and dequantization of variational quantum learning models".

2025 / 05

We posted the white paper "Quantum computing and artificial intelligence: status and perspectives" on arXiv.
I was invited to visit Juan Carrasquilla's group at ETH Zürich, where I gave a talk on "Kernel-based dequantization of variational QML without Random Fourier Features".

2025 / 04

I started a 4-month summer internship in Stefan Woerner's group, at IBM Research Europe, in Zürich.

2025 / 03

We posted "Kernel-based dequantization of variational QML without Random Fourier Features" on arXiv.
I gave a tutorial on "The quantum kernel saga" in our group's tutorial series, in Berlin.

2025 / 02

Our work "On the relation between trainability and dequantization of variational quantum learning models" was accepted as a poster in ICLR 2025!
Our work "Potential and limitations of random Fourier features for dequantizing quantum machine learning" was published in Quantum!
I visited Umesh Vazirani's group at Simons in Berkeley, where I gave a talk on "On the relation between trainability and dequantization of variational quantum learning models".
I visited John Preskill's group at Caltech in Pasadena, where I gave a talk on "On the relation between trainability and dequantization of variational quantum learning models".

2024

12

We posted "Opportunities and limitations of explaining quantum machine learning" on arXiv.

11

I attended the conference Quantum Techniques in Machine Learning (QTML2024) in Melbourne, where I gave a talk on "On the expressivity of embedding quantum kernels". My collaborator Adrián Pérez-Salinas gave a talk on "On the relation between trainability and dequantization of variational quantum learning models". My collaborator Jonas Naujoks gave a talk on "Opportunities and limitations of explaining quantum machine learning".

10

I gave a talk on "Machine Learning on Quantum Computers: How it started vs. How it's going" online at the BMW quantum computing community talk series.
I visited Eliška Greplová's group at TU Delft, where I gave a talk on "On the relation between trainability and dequantization of variational quantum learning models".
I was invited to visit the QAT team at INRIA Paris, where I gave a talk on "On the expressivity of embedding quantum kernels".

09

I attended the workshop Seeking Quantum Advantage (SeeQA 2024) in Oxford, where I presented a poster on "On the relation between trainability and dequantization of variational quantum learning models".

08

We posted "Concept learning of parameterized quantum models from limited measurements" on arXiv.
I attended the QMATH summer school on quantum simulation, in Copenhagen, where I presented a poster on "On the relation between trainability and dequantization of variational quantum learning models".

07

I started a three-month research visit in Vedran Dunjko's group, at Leiden University, as part of my Ellis PhD program.
I visited Bert Kappen's group at Radboud University, in Nijmegen, where I gave a talk on "On the relation between trainability and dequantization of variational quantum learning models".
I was invited to give a talk on "On the relation between trainability and dequantization of variational quantum learning models" online in the QTI Journal club at CERN.

04

Our work "On the expressivity of embedding quantum kernels" was published in Machine Learning: Science and Technology!
I attended the conference Quantum Computing Theory in Practice (QCTiP 2024) in Edinburgh, where I gave a talk on "Understanding quantum machine learning also requires rethinking generalization". I also presented a poster on "On the expressivity of embedding quantum kernels".
I was invited to visit the GIQ group at Universitat Autònoma de Barcelona, where I gave a talk on "On the expressivity of embedding quantum kernels".

03

Our work "Understanding quantum machine learning also requires rethinking generalization" was published in Nature Communications!
I attended the conference Frühjahrstagung der Deutschen Physikalischen Gesellschaft (DPG March Meeting) in Berlin, where I gave two talks, one on "Understanding quantum machine learning also requires rethinking generalization" and "On the expressivity of embedding quantum kernels".

02

I was invited to give a talk on "On the expressivity of embedding quantum kernels" online in the IBM Quantum (UK) seminar.

01

I attended the conference Quantum Information Processing (QIP 2024) in Taipei, where I presented a poster about "Understanding quantum machine learning also requires rethinking generalization".
I was invited to give a talk on "On the expressivity of embedding quantum kernels" in Singapore, in the QAISG QML seminar series.

2023

12

I was invited to visit Vedran Dunjko's group at Leiden University, where I gave a talk on "On the expressivity of embedding quantum kernels".

11

I attended the conference Quantum Techniques in Machine Learning (QTML 2023) in CERN, where I gave a talk on "Understanding quantum machine learning also requires rethinking generalization".
I was invited to visit Zoë Holmes' group at EPFL, where I gave a talk on "On the expressivity of embedding quantum kernels".

10

I was invited to give a talk on "Understanding quantum machine learning also requires rethinking generalization" online in the Google Quantum AI QML seminar.
I was invited to give a talk on "Understanding quantum machine learning also requires rethinking generalization" online in the QAISG QML seminar series.

09

I received the Google PhD Fellowship for Quantum Computing!
I was invited to visit the Quantic group at the Barcelona Supercomputing Center, where I gave a talk on "Understanding quantum machine learning also requires rethinking generalization".

04

I attended a workshop on Machine Learning and (Quantum) Physics in Obergurgl, where I gave a talk on "Exploiting symmetry in variational quantum machine learning".

03

Our work "Exploiting Symmetry in Variational Quantum Machine Learning" was published in PRX Quantum!

01

I was invited to visit the Quantic group at the Barcelona Supercomputing Center, where I gave a talk on "Exploiting symmetry in variational quantum machine learning".

2022

11

I attended the conference Quantum Techniques in Machine Learning (QTML 2022) in Naples, where I presented a poster on "Non-embedding quantum kernels: do they exist?".

10

Our work "Training quantum embedding kernels on near-term quantum computers" was published in PRA!

2021

11

Our work "Encoding-dependent generalization bounds for parametrized quantum circuits" was published in Quantum!
I attended the conference Quantum Techniques in Machine Learning (QTML 2021) online, where I gave a talk on "Encoding-dependent generalization bounds for parametrized quantum circuits".
I was invited to give a talk on "Encoding-dependent generalization bounds for parametrized quantum circuits" online in Marco Tomamichel's group seminar.

10

I was selected for the ELLIS PhD program, this marks the start of my PhD!

03

Our team won first place and the sponsor prize at QHACK 2021. This online event was organized by Xanadu.

2020

10

Our team won second place in the Qiskit Hackathon Global! This invite-only online event was organized by IBM Quantum.

03

I attended the conference Quantum Computing Theory in Practice (QCTiP 2020) online, where I presented a poster on "Data re-uploading for a universal quantum classifier".

02

Our work "Data re-uploading for a universal quantum classifier" was published in Quantum!

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