Juan Felipe Huan Lew Yee

Eduard Matito and Mario Piris from our lab presenting their recent work at QVEST2025 at Schloss Ringberg
@dipcehu.bsky.social
@ehu.eus

22.09.2025 10:40 — 👍 7    🔁 3    💬 0    📌 0

Advancing Natural Orbital Functional Calculations through Deep Learning-Inspired Techniques for Large-Scale Strongly Correlated Electron Systems Natural orbital functional (NOF) theory provides a valuable framework for studying strongly correlated systems at an affordable computational cost, with an accuracy comparable to highly demanding wave...

Check out the latest by @felipelewyee.bsky.social and Mario Piris from our lab
#compchem

journals.aps.org/prl/abstract...

20.05.2025 14:24 — 👍 5    🔁 2    💬 0    📌 0

Advancing Natural Orbital Functional Calculations through Deep Learning-Inspired Techniques for Large-Scale Strongly Correlated Electron Systems Natural orbital functional (NOF) theory provides a valuable framework for studying strongly correlated systems at an affordable computational cost, with an accuracy comparable to highly demanding wave...

🎉 Excited to share our latest publication in Physical Review Letters:
✨Advancing Natural Orbital Functional Calculations through Deep Learning-Inspired Techniques for Large-Scale Strongly Correlated Electron Systems✨

🚀 Find the full article here:
journals.aps.org/prl/abstract...

20.05.2025 15:02 — 👍 1    🔁 1    💬 0    📌 0

MOLGW version 3.4 is out! · molgw molgw · Discussion #48 What's new in version 3.4 Overview Complex wavefunctions X2C relativistic wavefunctions for HF/DFT calculations Possibility to link with LIBCINT whatever the p-orbital ordering Support for alternat...

MOLGW 3.4 is out! Want to use/test i) the G3W2 approx, ii)
complex molecular orbitals, iii) X2C relativistic wavefunctions for HF/DFT calculations, iv) use alternate compilers: LLVM flang/clang++ and Intel ifx/icx ? #compchem github.com/molgw/molgw/...

20.03.2025 09:51 — 👍 4    🔁 1    💬 1    📌 0

Julia Now Available on Google Colab Julia is now officially available on Google Colab, enabling high-performance computing, GPU acceleration, and seamless cloud-based data science.

Big news for Julia users! Julia is now officially supported in Google Colab, making high-performance computing & #GPU acceleration more accessible than ever. juliahub.com/blog/julia-n... #JuliaLang #GoogleColab #DataScience #AI #CloudComputing #coding #colab

12.03.2025 15:53 — 👍 17    🔁 5    💬 0    📌 1

Check out the latest by @daviddesancho.bsky.social and @xabierjota.bsky.social

28.02.2025 14:53 — 👍 1    🔁 1    💬 0    📌 0

Efficient Energy Measurement of Chemical Systems via One-Particle Reduced Density Matrix: A NOF-VQE Approach for Optimized Sampling In this work, we explore the use of the one-particle reduced density matrix (1RDM) to streamline energy measurements of chemical systems on quantum computers, particularly within the variational quant...

Check out the latest by Juan Felipe Huan Lew-Yee and Mario Piris from our laboratory
@dipcehu.bsky.social

pubs.acs.org/doi/10.1021/...

24.02.2025 11:10 — 👍 3    🔁 3    💬 0    📌 0

Efficient Energy Measurement of Chemical Systems via One-Particle Reduced Density Matrix: A NOF-VQE Approach for Optimized Sampling In this work, we explore the use of the one-particle reduced density matrix (1RDM) to streamline energy measurements of chemical systems on quantum computers, particularly within the variational quantum eigensolver (VQE) framework. This approach leverages the existence of an exact energy functional of the 1RDM, enabling a reduction in both the number of expectation values to measure and the number of circuits to execute, thereby optimizing quantum resource usage. Specifically, sampling the 1RDM involves measuring only elements, which is significantly fewer than the required for the Hamiltonian’s expectation value ⟨Ĥ⟩. We demonstrate our approach by harnessing the well-established natural orbital functional (NOF) theory, using the natural orbitals and occupation numbers derived from the diagonalization of the 1RDM measured from the quantum computer. Starting with the H2 system, we validate the accuracy of our method by comparing the energy derived from NOF approximations applied to the exact wave function with the value obtained from ⟨Ĥ⟩. This is followed by an optimization of the gate parameters by minimizing the energy using the NOF approximations as the objective function. The analysis is extended to larger systems, such as LiH, Li2, OH–, FH, NeH+, and F2 using a wave function ansatz with single and double excitation gates. This NOF-based method reduces the scaling cost of circuit executions compared to standard VQE implementations, achieving around 90% savings in the systems used in this work. Overall, by using a well-performing NOF as the objective function, the proposed NOF-VQE demonstrates the viability of NOF approximations for obtaining accurate energies in the noisy intermediate-scale quantum era and underscores the potential for developing new functionals tailored to quantum computing applications.

✨ Excited to share our new paper: "Efficient Energy Measurement of Chemical Systems via One-Particle Reduced Density Matrix: A NOF-VQE Approach for Optimized Sampling" by Felipe Lew-Yee and Mario Piris

⚛️ Read the full paper here: pubs.acs.org/doi/10.1021/...

24.02.2025 11:49 — 👍 4    🔁 2    💬 1    📌 0

GitHub - DoNOF/DoNOFsw: Donostia Natural Orbital Functional Software Donostia Natural Orbital Functional Software. Contribute to DoNOF/DoNOFsw development by creating an account on GitHub.

Open-source software to carry-out NOFT calculations on molecules (energy, geom opt ...); strong-correlation friendly!

code: github.com/DoNOF/DoNOFsw
documentation: donof-documentation.readthedocs.io/en/latest/in...

#compchem

25.11.2024 16:05 — 👍 5    🔁 1    💬 0    📌 0