cryptomite


Namecryptomite JSON
Version 0.1.2 PyPI version JSON
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home_page
SummaryAn awesome extractor library
upload_time2024-01-24 14:17:45
maintainer
docs_urlNone
authorCambridge Quantum
requires_python>=3.8
license
keywords quantum quantum computing
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # cryptomite

`cryptomite` is a modular, extensible high-level Python library 
for randomness extractions, created by Quantinuum's Quantum Cryptography team. 
At a high level, the library offers state-of-the-art randomness extractors that are easy to use, optimized and numerically precise
providing a trade-off of features that suits numerous practical use cases today.

The performance critical parts of the library (e.g. NTT) are implemented in C++, but the rest of the
library (e.g. parameter estimation) is implemented in Python for accessibility and ease of installation.

The package is available for Python 3.8 and higher on Mac, Windows and Linux. To install, type:

```bash 
pip install cryptomite
```



## Example Usage

```python
from cryptomite.trevisan import Trevisan
from random import randint

n, m, max_eps = 1000, 200, 0.01

ext = Trevisan(n, m, max_eps)

input_bits = [randint(0, 1) for _ in range(n)]
seed_bits = [randint(0, 1) for _ in range(ext.ext.get_seed_length())]

output_bits = ext.extract(input_bits, seed_bits)
```

## Documentation

To build the docs, run

```bash
cd docs
pip install -r requirements.txt
make clean
make html
```

## Testing

Install `pytest`, then run `pytest test`.

To run the C++ tests, run

```bash
cmake .
make
test/runTest
```

            

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    "description": "# cryptomite\n\n`cryptomite` is a modular, extensible high-level Python library \nfor randomness extractions, created by Quantinuum's Quantum Cryptography team. \nAt a high level, the library offers state-of-the-art randomness extractors that are easy to use, optimized and numerically precise\nproviding a trade-off of features that suits numerous practical use cases today.\n\nThe performance critical parts of the library (e.g. NTT) are implemented in C++, but the rest of the\nlibrary (e.g. parameter estimation) is implemented in Python for accessibility and ease of installation.\n\nThe package is available for Python 3.8 and higher on Mac, Windows and Linux. To install, type:\n\n```bash \npip install cryptomite\n```\n\n\n\n## Example Usage\n\n```python\nfrom cryptomite.trevisan import Trevisan\nfrom random import randint\n\nn, m, max_eps = 1000, 200, 0.01\n\next = Trevisan(n, m, max_eps)\n\ninput_bits = [randint(0, 1) for _ in range(n)]\nseed_bits = [randint(0, 1) for _ in range(ext.ext.get_seed_length())]\n\noutput_bits = ext.extract(input_bits, seed_bits)\n```\n\n## Documentation\n\nTo build the docs, run\n\n```bash\ncd docs\npip install -r requirements.txt\nmake clean\nmake html\n```\n\n## Testing\n\nInstall `pytest`, then run `pytest test`.\n\nTo run the C++ tests, run\n\n```bash\ncmake .\nmake\ntest/runTest\n```\n",
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