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Description:

quantumrandom 1.4.1

Quantum random numbers in Python

Use the Python random module with real quantum random numbers from
ANU. The default pseudo-random generator is replaced by calls to
the ANU API.
Usage
Import qrandom and use it like the standard random module. For example:
>>> import qrandom

>>> qrandom.random()
0.15357449726583722

>>> qrandom.sample(range(10), 2)
[6, 4]

>>> qrandom.gauss(0.0, 1.0)
-0.8370871276247828

Alternatively, you can use the class qrandom.QuantumRandom. It has the same
interface as random.Random.
There is also a NumPy interface, although it is not fully tested:
>>> from qrandom.numpy import quantum_rng

>>> qrng = quantum_rng()

>>> qrng.random((3, 3)) # use like numpy.random.default_rng()
array([[0.37220278, 0.24337193, 0.67534826],
[0.209068 , 0.25108681, 0.49201691],
[0.35894084, 0.72219929, 0.55388594]])

NumPy is supported using RandomGen.
Installation
The minimum supported Python version is 3.9. Install with pip:
pip install -U quantum-random

If you want NumPy support:
pip install -U 'quantum-random[numpy]'

First-time setup: setting your API key
ANU requires you to use an API key. You can get a free trial or pay for a key
here.
You can pass your key to qrandom in three ways:

By setting the environment variable QRANDOM_API_KEY.
By running the included command line utility qrandom-init to save your
key in qrandom.ini in a subdirectory of your home config directory
as specified by XDG, e.g., /home/<your-username>/.config/qrandom/.
By running qrandom-init to save your key in qrandom.ini in a directory
of your choice, and then specifying this directory by setting
QRANDOM_CONFIG_DIR.

If QRANDOM_API_KEY is set, its value is used as the API key and the
config file is not read. Otherwise, qrandom will look for the key
in the config directory. The config directory defaults to the XDG home config
and can be changed by setting QRANDOM_CONFIG_DIR.
Pre-fetching batches
Batches of quantum numbers are fetched from the API as needed.
Each batch contains 1024 numbers. Use qrandom.fill(n) to fetch n batches
if you need to pre-fetch at the start of your computation.
Tests
The tests run for Python 3.9 - 3.12 on the latest Windows,
macOS and Ubuntu runner images.
See here for a visualisation and a Kolmogorov–Smirnov
test.
Notes on implementation
The qrandom module exposes a class derived from random.Random with a
random() method that outputs quantum floats in the range [0, 1)
(converted from 64-bit integers). Overriding random.Random.random
is sufficient to make the qrandom module behave mostly like the
random module as described in the Python docs. The exceptions
are getrandbits() and randbytes(): these are not available in
qrandom. Because getrandbits() is not available, randrange() cannot
produce arbitrarily long sequences. Finally, the user is warned when seed()
is called because the quantum generator has no state. For the same reason,
getstate() and setstate() are not implemented.
License
See LICENCE.