Metadata-Version: 2.1
Name: qoalarandom
Version: 1.0.5
Summary: iQuHack 2023, CovalentxIBM Challenge
Home-page: https://github.com/iQuHack-2023-Qoalas/qRandom.git
Author: Qoalas Team
Author-email: hannah.yelle@maine.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE.txt

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[![License](https://img.shields.io/github/license/Qiskit/qiskit.svg?)](https://opensource.org/licenses/Apache-2.0)
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**QoalaRandom** is a quantum powered random number generator. 

Produced by Qoalas @ iQuHack 2023

# Installation

The best way of installing `qoalarandom` is by using `pip`

```bash
$ pip install -i https://test.pypi.org/simple/ qoalarandom
```

# Using Qoala Random 

## randint(start, stop, distribution=1)

Parameters:
- start (int)
- stop (int)
- distribution (int, default=1 (uniform distribution))

    distribution is the index of the distribution type desired. See more in distribution options. 

Returns: A random integer between start and stop 

## randrange(start, stop, step=1, distribution=1)

Parameters: 
- start (int)
- stop (int)
- step (int, default=1)
- distribution (int, default=1 (uniform distribution))

    distribution is the index of the distribution type desired. See more in distribution options.

Returns: A random integer between start and stop incrementing by step

## randfloat(start, stop, distribution=1)

Parameters: 
- start (int)
- stop (int)
- distribution (int, default=1 (uniform distribution))

    distribution is the index of the distribution type desired. See more in distribution options.

Returns: A random float between start and stop 

## randchoice(user_list, distribution=1)

Parameters:
- user_list (list)
- distribution (int, default=1 (uniform distribution))

    distribution is the index of the distribution type desired. See more in distribution options.

Returns: A random element of user_list

# Distribution Options
## 0 = normal
To extract the random numbers from a normal distribution (centred at 0, variance = 1)
## 1 = uniform 
To extract the random numbers from a uniform distribution
## 2 = porterthomas 
To extract the random numbers from a Porter-Thomas distribution
## 3 = deeprandom
To fully take advantage of the quantum circuit, creating a circuit that exploit statistical propertier of dual-unitary circuits to generate a number extracted uniformly from the Haar distribution (approximated according to the amount of resources requested to the QPU)

