quantum-random 1.4.1

Quantum random numbers

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:

1. By setting the environment variable QRANDOM_API_KEY.
2. 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/.
3. 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.