Josep Lumbreras

Josep Lumbreras

A researcher learning in quantum environments

About

I am Josep Lumbreras, a Research Fellow at Nanyang Technological University (Singapore), where I work in the group of Prof. Mile Gu. I completed my Ph.D. at the Centre for Quantum Technologies, National University of Singapore, under the supervision of Prof. Marco Tomamichel. Before that, I obtained a double major in Physics and Mathematics as well as a Master’s degree in Particle Physics and Gravitation from the University of Barcelona.

Research interests

In general, my research interests lie in (quantum) learning theory.

My main line of work is at the intersection of reinforcement learning and quantum information, where I aim to establish rigorous theoretical results. I am particularly interested in applying reinforcement learning to quantum settings, including adaptive measurements, quantum control, and thermodynamic agents. Within the broad landscape of RL frameworks, I have focused especially on the multi-armed bandit problem and its applications to quantum tasks.

I also study the quantum generalization of (Partially Observable) Hidden Markov Models, investigating both their fundamental aspects and practical applications. These models provide a foundation for describing stochastic processes, quantum states with finite correlations, and several reinforcement learning frameworks with quantum data.

Feel free to reach out by email if you would like to discuss these topics or explore potential collaborations.

Publications

  1. Bandits roaming Hilbert space
    J. Lumbreras.
    [arXiv (2025)]

  2. Quantum state-agnostic work extraction (almost) without dissipation
    J. Lumbreras, R.C. Cheng, Y. Hu, M. Gu, M. Tomamichel.
    [QTML2025][AQIS25][Quantum Resources (2025)][arXiv (2025)]

  3. Learning pure quantum states (almost) without regret
    J. Lumbreras, M. Terekhov, M. Tomamichel.
    [QTML2025][INFORMS2025][AQIS24 (long talk)][arXiv (2024)]

  4. Linear bandits with polylogarithmic minimax regret
    J. Lumbreras, M. Tomamichel.
    [COLT(2024)][arXiv (2024)]

  5. Learning finitely correlated states: stability of the spectral reconstruction
    M. Fanizza, N. Galke, J. Lumbreras, C. Rouzé, A. Winter.
    [QuARC workshop 2025][Beyond IID 2024][arXiv (2024)]

  6. Quantum Theory in Finite Dimension Cannot Explain Every General Process with Finite Memory
    M. Fanizza†, J. Lumbreras† (co-first author), A. Winter.
    [Communications in Mathematical Physics][TQC 23][Beyond IID 2022][arXiv (2023)]

  7. Quantum contextual bandits and recommender systems for quantum data
    S. Brahmachari†, J. Lumbreras† (co-first author), M. Tomamichel.
    [Quantum Machine Intelligence][arXiv (2023)]

  8. Multi-armed quantum bandits: Exploration versus exploitation when learning properties of quantum states
    J. Lumbreras, E. Haapasalo, M. Tomamichel.
    [Quantum][AQIS 21 (long talk)][Beyond IID 2021][QTML 21][arXiv (2022)]

  9. Scaling of variational quantum circuit depth for condensed matter systems
    C. Bravo†, J. Lumbreras† (co-first author), L. Tagliacozzo, J. I. Latorre.
    [Quantum][arXiv (2020)]