QAILab
About project
QAILab is a high level library, which enables the user to integrate quantum neural networks of different configurations into their existing PyTorch neural networks.
The library aims to support a large amount of backends from different quantum computer manufacturers, such as IBM, AQT and Orca Computing, via its integration with Quantum Launcher.
Installation
You can install QAILab via pip:
pip install qailab
Examples and tutorials
Examples and tutorials are available in the QAILab documentation
1 maintainer
Metadata
1 maintainer
Release files for QAILab 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| qailab-0.1.2.tar.gz | 20.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| qailab-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 48.5 kB
Release files / qailab-0.1.2.tar.gz
| Download URL | qailab-0.1.2.tar.gz |
|---|---|
| Size | 20.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.12
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Release files / qailab-0.1.2-py3-none-any.whl
| Download URL | qailab-0.1.2-py3-none-any.whl |
|---|---|
| Size | 27.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.12
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