VQE-LD tutorial#1771
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Hi @ericoteixeira thanks for opening this PR. It looks like you have included your Python virtual environment. Could you please delete this from the PR? In addition, do you have a notebook or python script for the VQE-LD demo? |
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Hi @josh146,
I just removed the environment and updated the PR.
With respect to a python script, I attached a file.
Best Regards
On 7 May 2026, at 12:11, Josh Izaac ***@***.***> wrote:
josh146
left a comment
(PennyLaneAI/demos#1771)
<#1771 (comment)>
Hi @ericoteixeira <https://github.com/ericoteixeira> thanks for opening this PR. It looks like you have included your Python virtual environment. Could you please delete this from the PR?
In addition, do you have a notebook or python script for the VQE-LD demo?
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Hi PennyLane team, I would like to follow up on this PR kindly. The demo is based on our recent Journal of Computational Chemistry publication, and I would be happy to address any feedback or make any required modifications. |
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Hi @ericoteixeira , thanks for your submission! I wanted to quickly follow up on this PR, we had a discussion and think it might be well suited for a community demo! Community demos have a few advantages:
You can find the submission guidelines for the community demo at the bottom of our demo submission page. This process will require you to open an issue so we’ll get back to you on the issue once you’ve opened it. Is this something you would be interested in? Let us know if you have any questions about this! |
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Hi @nobese, Thank you so much for the feedback and for considering this work for a community demo! It sounds like a great opportunity to share the project with the PennyLane community, and I am definitely interested in exploring this path. Before I proceed with opening the issue, could you provide a bit more insight into the criteria used to make this decision? I would love to understand if there were specific technical requirements, scope limitations, or architectural reasons why it fits better as a community demo rather than a core PennyLane demo. This feedback would be incredibly valuable for improving this project and tailoring future contributions. I'll review the submission guidelines in the meantime and look forward to your thoughts! |
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Hi @ericoteixeira! There are a few reasons here. On one hand, since this is your first PennyLane demo contribution, it’s better to start with a process that is simpler! Core PennyLane demos require more back and forth on GitHub and it’s not the easiest when it’s your first time contributing a demo. Community demos on the other hand are straightforward: you host your demo on your own repo or Google Colab and we handle the upload to the community page. All you need to do is open an issue with some key info and respond some questions in the comments of that issue. On a more editorial side, while we recognize that there’s quality work being done on variational algorithms and estimation of ground state energies, our current focus is more in FTQC and not so much on NISQ, so we prioritize the ‘core’ demos for work that goes in this direction. You can read more about our quantum chemistry focus on our blog on material discovery with quantum dynamics. All of this being said, we’re very happy to see a variety of projects and papers built using PennyLane and we’re happy to highlight them on PennyLane’s social media! If you decide to contribute this as a community demo we can talk more about this in the issue once you open it. Let us know if you have any further questions : ) |
3 participants
Title:
An overview of VQE-LD
Summary:
Improving ground state energy prediction by incorporating the convergence of the one-particle reduced density matrix in VQE.
Relevant references:
Amanda Marques de Lima, Erico Souza Teixeira, Eivson Darlivam Rodrigues de Aguiar Silva, Ricardo Luiz Longo. Improving Energy and Molecular Properties by Convergence of the One-Particle Reduced Density Matrix in Variational Quantum Eigensolvers (VQE). Journal of Computational Chemistry 47 (2026). https://doi.org/10.1002/jcc.70289
Possible Drawbacks:
Related GitHub Issues:
If you are writing a demonstration, please answer these questions to facilitate the marketing process.
GOALS — Why are we working on this now?
Present a variation of VQE, now incorporating the one-particle reduced density matrix (1-RDM) in the convergence process.
AUDIENCE — Who is this for?
Chemistry researchers in quantum computing.
KEYWORDS — What words should be included in the marketing post?
Density matrix, quantum computing, variational quantum eigensolver
Which of the following types of documentation is most similar to your file?