PAGE UNDER CONSTRUCTION
quantum circuit dataset - this repository will be soon completed
===> dataset is already accessible!
There is already the dataset and qml experiments, some guide can be found in doc/sources
MNISQ-quantum-circuit-dataset
This library provides machine learning datasets as a Qulacs's QuantumCircuit instance.
How To Install
pypi
pip install mnisq
source install
pip install git+https://github.com/FujiiLabCollaboration/MNISQ-quantum-circuit-dataset.git
Directory structure
doc/source/notebooksprovides sample usage of this library.mnisqandtestscontains python code.generator_scriptscontains scripts used to generate datasets.
Contributor
This project was developed by:
- Koki Aoyama(@kotamanegi)
- Hayata Morisaki
- Kouki Kawamura(@KowerKoint)
- Toshio Mori(@forest1040)
- Leonardo Placidi(@Gruntrexpewrus)
- Ryuichiro Hataya
- Kosuke Mitarai
- Keisuke Fujii
Metadata
Release files for mnisq 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mnisq-0.2.0.tar.gz | 21.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mnisq-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 49.8 kB
Release files / mnisq-0.2.0.tar.gz
| Download URL | mnisq-0.2.0.tar.gz |
|---|---|
| Size | 21.6 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / mnisq-0.2.0-py3-none-any.whl
| Download URL | mnisq-0.2.0-py3-none-any.whl |
|---|---|
| Size | 28.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.17
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