Reproduce arXiv:2207.05612

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
35/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
matplotlib, python
Domain
machine-learning

Research direction

Start by reading the linked paper, especially Figure 2(a), and identify the DMRG and finite-fidelity methods needed to reproduce it. Then check examples/reproduce_papers/ for an existing reproduction and use the requested folder structure and meta.yaml fields. Compare small systems against exact tc.Circuit results; done means a generated figure or terminal output matching the paper, with Black and Pylint checks confirmed.

Written by the indexing model from the issue text.

Description

good first issue

1. Paper Information

  • Title: A density-matrix renormalization group algorithm for simulating quantum circuits
    with a finite fidelity

  • Link: https://arxiv.org/abs/2207.05612

  • Tags: DMRG, matrix product state, classical simulation

2. Reproduction Goal

Which specific figure or table from the paper needs to be reproduced?

  • Target: Fig 2(a)

  • Expected Metric/Result: also compare with exact tc.Circuit results for small size systems to ensure the correctness

  • Allow to reduce the problem size compared to the paper: Yes, could use reduced qubit counts

3. Directory Structure Plan

To keep the repository organized, please strictly follow this folder structure:

  • Proposed Folder Name: examples/reproduce_papers/<YYYY>_<keywords>/
    • Naming Convention: year_keywords
    • Example: examples/reproduce_papers/2023_quantum_transformer/

4. Metadata Definition (meta.yaml)

Please provide the content for the meta.yaml file that will be placed in the folder.

Contributors/AI: Copy and fill this block directly into the meta.yaml file.

title: ""
arxiv_id: ""
url: ""
year: 
authors: 
  - ""
  - ""
tags:
  - ""
  - ""
hardware_requirements:
  gpu: False
  min_memory: ""
description: ""
outputs:
  - target: "Figure 2(a)"
    path: result.png
    script: "main.py"

5. Implementation Requirements

For AI Assistants / Contributors: Please strictly follow the rules below.

  • Docstring Standard:

    • The file MUST start with a docstring containing the paper title and the link.
      Format example:

      """
Reproduction of "Paper Title"
Link: https://arxiv.org/abs/xxxx.xxxxx

Description:
This script reproduces Figure 3 from the paper using TensorCircuit-NG.
"""

  • Outputs:
    The reproduced data and figures should be in outputs/ subfolder.

  • Subfolder structure:

    examples/reproduce_papers/<YYYY>_<Keywords>/
├── meta.yaml
├── main.py
├── ...       # models, data, etc.
└── outputs/
    ├── result.png    # used for gallery
    └── ...           # other outputs if necessary

  • Code Quality & Linter:

    • Formatter: The code must be formatted using black.
    • Linter: The code must pass pylint checks (clean code, handle errors).
    • Type Hints: Standard type is NOT REQUIRED.

  • Dependencies:

    • Use tensorcircuit as the main framework.
    • Use matplotlib for plotting if necessary.

6. Verification

To be considered complete, please provide:

  1. A screenshot of the generated plot or the terminal output matching the paper's result.
  2. Confirmation that black and pylint have been run.
# Example verification commands
black examples/reproduce_papers/<paper_subfolder>/*.py
pylint examples/reproduce_papers/<paper_subfolder>/*.py

7. Checklist

  • I have checked that this paper hasn't been reproduced in the repo yet.

  • The script is self-contained and runnable.

  • The docstring includes the correct arXiv/DOI link.

  • black formatting applied.

  • pylint check passed.

Dominant language
Python
Stars
92
Forks
25
Avg merge
21h 12m
Merged PRs (30d)
57

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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