maqip

Many Qsvms In Parallel

https://github.com/rmoretti9/maqip

Science Score: 44.0%

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  • Scientific vocabulary similarity
    Low similarity (5.8%) to scientific vocabulary
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Repository

Many Qsvms In Parallel

Basic Info
  • Host: GitHub
  • Owner: rmoretti9
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 150 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created about 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

MaQIP

Many Qsvms In Parallel

Run Parallel QSVM on QPU example

Prepare a Python environment and activate it. (It should work with any Python version > 3.8). For example, through venv:

python -m venv <environment_name>

Clone the Repository

Clone the MaQIP repository and install the maqip package:

git clone https://github.com/rmoretti9/MaQIP.git

cd MaQIP
pip install -e .

Configure IBM Account

Inside MaQIP/examples/torino_run.py (right after the imports), fill the IBM account/provider fields with your token/instance as required and save the changes.

Run the Example Script

Move inside the examples folder (this is important for the correct execution) and run the script. This will prepare the Quantum Circuits and send batch jobs to IBM Torino:

cd examples
python torino_run.py

This script will generate a folder called Output_genetic that will progressively save data as they are generated. Do not rename the Output_genetic folder and its content.

Owner

  • Name: Roberto Moretti
  • Login: rmoretti9
  • Kind: user
  • Location: Milan
  • Company: University of Milan Bicocca

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Moretti"
  given-names: "Roberto"
  orcid: "https://orcid.org/0000-0002-5201-5920"

title: "MaQIP"
version: 0.1
doi: 10.5281/zenodo.14917911
date-released: 2025-02-24
url: "https://github.com/rmoretti9/MaQIP"

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Dependencies

pyproject.toml pypi
requirements.txt pypi
  • numpy ==2.2.0
  • pandas ==2.2.3
  • pygad ==3.3.1
  • qiskit ==1.1.0
  • qiskit_aer ==0.14.2
  • qiskit_ibm_provider ==0.11.0
  • qiskit_ibm_runtime ==0.24.0
  • qiskit_machine_learning ==0.7.2
  • scikit_learn ==1.5.2