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.github/ISSUE_TEMPLATE/bug_report.md

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---
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name: Bug report
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about: Create a report to help us improve
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title: ''
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labels: ''
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assignees: ''
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---
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**Describe the bug**
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A clear and concise description of what the bug is. If you are referring to methods reported in papers please add a link to the paper (preferably Arxiv).
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**To Reproduce**
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Steps to reproduce the behavior: Ideally an executable code snipped like
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```python
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import tequila as tq
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U = tq.gates.Ry(angle="a", target=0)
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H = tq.paulis.X(0)
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E = tq.ExpectationValue(H=H, U=U)
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energy = tq.compile(E)
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eval = energy()
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```
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**Expected behavior**
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A clear and concise description of what you expected to happen or what you want to do.
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**Computer (please complete the following information):**
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- OS: [e.g. iOS/Linux/Windows]
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- Version [Python and Tequila]
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Get is via
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```
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import sys, platform
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import tequila as tq
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print("tequila version: ", tq.__version__)
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print("python version: ", sys.version)
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print("platform: ", platform.version())
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```
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**Smartphone (please complete the following information):**
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- Device: [e.g. iPhone6]
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- OS: [e.g. iOS8.1]
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- Browser [e.g. stock browser, safari]
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- Version [e.g. 22]
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**Additional context**
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Add any other context about the problem here.
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---
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name: Feature request
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about: Suggest an idea for this project
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title: ''
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labels: ''
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assignees: ''
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---
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**Is your feature request related to a problem? Please describe.**
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A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
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**Describe the solution you'd like**
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A clear and concise description of what you want to happen.
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**Describe alternatives you've considered**
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A clear and concise description of any alternative solutions or features you've considered.
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**Additional context**
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Add any other context or screenshots about the feature request here.

CONTRIBUTING.md

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# How to contribute
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If you find any bugs or inconveniences in `tequila` please don't be shy and let us know.
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You can do so either by raising an issue here on github or contact us directly.
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If you already found a solution you can contribute to `tequila` over a pull-request.
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Here is how that works:
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1. Make a fork of `tequila` to your own github account.
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2. Checkout the `devel` branch and make sure it is up to date with the main [github repository](https://github.com/aspuru-guzik-group/tequila).
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3. Create and checkout a new branch from `devel` via `git branch pr-my-branch-name` followed by `git checkout pr-my-branch-name`. By typing `git branch` afterwards you can check which branch is currently checked out on your computer.
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4. Introduce changes to the code and commit them with git.
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5. Push the changes to *your* github account
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6. Log into github and create a pull request to the main [github repository](https://github.com/aspuru-guzik-group/tequila). The pull-request should be directed to the `devel` branch (but we can also change that afterwards).
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If you plan to introduce major changes to the base library it can be beneficial to contact us first.
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This way we might be able to avoid conflicts before they arise.
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If you used `tequila` for your research, feel free to include your algorithms here, either by integrating it into the core libraries or by demonstrating it with a notebook in the tutorials section. If you let us know about it, we will also add your research article in the list of research projects that use tequila (see the github repo
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s README).

README.md

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When to Reject a Ground State Preparation Algorithm
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[arxiv:2212.09492](https://doi.org/10.48550/arXiv.2212.09492)
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R.P. Pothukuchi, L. Lufkin, Y.J. Shen, A. Simon, R. Thorstenson, B.E. Trevisan, M. Tu, M. Yang, B. Foxman, V. S. Pothukuchi, G. Epping, B. J. Jongkees, T.-H. Kyaw, J. R. Busemeyer, J. D Cohen, A. Bhattacharjee
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Quantum Cognitive Modeling: New Applications and Systems Research Directions
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[arxiv:2309.00597](https://arxiv.org/abs/2309.00597)
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T.-H. Kyaw, M. B. Soley, B. Allen, P. Bergold, C. Sun, V.S. Batista and A. Aspuru-Guzik
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Boosting quantum amplitude exponentially in variational quantum algorithms
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[10.1088/2058-9565/acf4ba](doi.org/10.1088/2058-9565/acf4ba)
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A.G. Cadavid, I. Montalban, A. Dalal, E. Solano, N.N. Hegade
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Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization
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[arxiv:2308.15475](https://arxiv.org/abs/2308.15475)
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Let us know, if you want your research project and/or tutorial to be included in this list!
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