Scientific Software and Algorithms

Software engineering for quantum computing ⬇

In my current role as a software engineer at Algorithmiq, I implement high-performance hybrid quantum-classical algorithms based on tensor-network methods. I also help establish sound coding practices and robust software design across the company.

Low-scaling MBPT algorithms ⬇

During my second postdoc, in Prof. Dorothea Golze's Large-Scale Theoretical Spectroscopy Emmy Noether group (LSTS) at TU Dresden, I worked on scalable many-body perturbation theory methods for core-level spectroscopy. My main contribution was the design of low-scaling GW algorithms for predicting XPS and XAS spectra on emerging exascale systems. I implemented these algorithms in FHI-aims and in the GreenX library of the NOMAD CoE. An introductory GW talk and a poster provide further context.

Massively parallel quantum Monte Carlo algorithms ⬇

I completed my first postdoc as part of the TREX CoE, in the Computational Chemical Physics (CCP) group of the University of Twente. This European project developed open-source, high-performance quantum chemistry software for emerging exascale architectures. As a member of Prof. Claudia Filippi's group, I contributed to the Cornell-Holland Ab-initio Materials Package (CHAMP), using techniques such as the Implicit Reference to Parameters method (IRP). I later gave this introductory QMC lecture to master's students in M19 (MRBO02) at TU Dresden.

Nuclear quantum dynamics and tensor decomposition algorithms ⬇

I obtained my PhD in physics at the University of Lille, under the supervision of Prof. Daniel Peláez. The subject of my thesis was the development and application of optimization and tensor decomposition algorithms for representing potential energy surfaces. These representations were used in nuclear quantum dynamics calculations with the Multiconfiguration Time-dependent Hartree (MCTDH) method. I also prepared an introductory MCTDH talk for the CCP group at the University of Twente.

Publications

1. Slootman, E., Chilkuri, V. G., Delval, A., et al., featuring Panadés-Barrueta, R. L. (2026). QMCkl: A Kernel Library for Quantum Monte Carlo Applications,
   The Journal of Chemical Physics, 164, 112501 (arXiv:2512.16677).
2. Leucke, M., Panadés-Barrueta, R. L., Bas, E. E., and Golze, D. (2025). Analytic continuation component of the GreenX library: robust Padé approximants with symmetry constraints,
   Journal of Open Source Software, 10(109), 7859.
3. Abbott, J. W., Mera Acosta, C., Akkoush, A., et al., featuring Panadés-Barrueta, R. L. (2025). Roadmap on Advancements of the FHI-aims Software Package,
   arXiv:2505.00125.
4. Panadés-Barrueta, R. L., Duflot, D., Soto, J., Martínez-Núñez, E., and Peláez, D. (2024). Automatic determination of the non‐covalent stable conformations of the NO2‐Pyrene cluster in full dimensionality (81D) using the vdW‐TSSCDS approach,
   ChemPhysChem, e202301001.
5. Azizi, M., Wilhelm, J., Golze, D., Delesma, F. A., Panadés-Barrueta, R. L., Rinke, P., Giantomassi, M. and Gonze, X. (2024). Validation of the GreenX library time-frequency component for efficient GW and RPA calculations,
   Physical Review B, 109, 245101 (arXiv:2403.06709).
6. Azizi, M., Wilhelm, J., Golze, D., Giantomassi, M., Panadés-Barrueta, R. L., Delesma, F. A., Buccheri, A., Gulans, A. Rinke, P., Draxl, C. and Gonze, X. (2023). Time-frequency component of the GreenX library: minimax grids for efficient RPA and GW calculations,
   Journal of Open Source Software, 8(90), 5570.
7. Panadés-Barrueta, R. L., Nadoveza, N., Gatti, F., and Peláez, D. (2023). On the sum-of-products to product-of-sums transformation between analytical low-rank approximations in finite basis representation,
   The European Physical Journal Special Topics, 1-8.
8. Panadés-Barrueta, R. L. and Golze, D. (2023). Accelerating core-level GW calculations by combining the contour deformation approach with the analytic continuation of W,
   Journal of Chemical Theory and Computation 19 (16), 5450–5464 (arXiv:2305.15955).
9. Nadoveza, N., Panadés-Barrueta, R. L., Shi, L., Gatti, F., and Peláez, D. (2023). Analytical high-dimensional operators in canonical polyadic finite basis representation (CP-FBR),
   The Journal of Chemical Physics, 158, 114109.
10. Shepard, S., Panadés-Barrueta, R. L., Moroni, S., Scemama A., and Filippi, C. (2022). Double excitation energies from quantum Monte Carlo using state-specific energy optimization,
    Journal of Chemical Theory and Computation, 18, 11, 6722–6731 (arXiv:2207.12160).
11. Panadés-Barrueta, R. L. and Peláez, D. (2020). Low-Rank Sum-of-Products Finite-Basis-Representation (SOP-FBR) of Potential Energy Surfaces,
    The Journal of Chemical Physics, 153, 234110.
12. Panadés-Barrueta, R. L., Martínez-Núñez, E., & Peláez, D. (2019). Specific Reaction Parameter Multigrid POTFIT (SRP-MGPF): Automatic Generation of Sum-of-Products Form Potential Energy Surfaces for Quantum Dynamical Calculations,
    Frontiers in Chemistry, 7, 576. Included in the book Application of Optimization Algorithms in Chemistry.
13. Panadés-Barrueta, R. L., Rubayo-Soneira, J., Monnerville, M., Larregaray, P., Dayou, F., and Rivero-Santamaría, A. (2016). Mean Potential Phase Space Theory study of the Si(3P) + OH(X2Π) → SiO(X1Σ+) + H(2S) reaction,
    Revista Cubana de Física, 33(2), 102-117.

Doctoral dissertation

Full quantum simulations of the interaction between atmospheric molecules and model soot particles, Panadés-Barrueta, R. L. (2020).
University of Lille, theses.fr.