qubolite 0.6.8

Toolbox for quadratic binary optimization

qubolite

A light-weight toolbox for working with QUBO instances in NumPy.

Installation

pip install qubolite

This package was created using Python 3.10, but runs with Python >= 3.8.

Usage Examples

By design, qubolite is a shallow wrapper around numpy arrays, which represent QUBO parameters. The core class is qubo, which receives a numpy.ndarray of size (n, n). Alternatively, a random instance can be created using qubo.random().

>>> import numpy as np
>>> from qubolite import qubo
>>> arr = np.triu(np.random.random((8, 8)))
>>> Q = qubo(arr)
>>> Q2 = qubo.random(12, distr='uniform')

By default, qubo() takes an upper triangle matrix. A non-triangular matrix is converted to an upper triangle matrix by adding the lower to the upper triangle.

To get the QUBO function value, instances can be called directly with a bit vector. The bit vector must be a numpy.ndarray of size (n,) or (m, n).

>>> x = np.random.random(8) < 0.5
>>> Q(x)
7.488225478498116
>>> xs = np.random.random((5,8)) < 0.5
>>> Q(xs)
array([5.81642745, 4.41380893, 11.3391062, 4.34253921, 6.07799747])

Version Log

• 0.2 Added problem embeddings (binary clustering, subset sum problem)
• 0.3 Added QUBOSample class and sampling methods full and gibbs
• 0.4 Renamed QUBOSample to BinarySample; added methods for saving and loading QUBO and Sample instances
• 0.5 Moved gibbs to mcmc and implemented true Gibbs sampling as gibbs; added numba as dependency
  • 0.5.1 changed keep_prob to keep_interval in Gibbs sampling, making the algorithm's runtime deterministic; renamed sample to random in QUBO embedding classes, added MAX 2-SAT problem embedding
• 0.6 Changed Python version to 3.8; removed bitvec dependency; added scipy dependency required for matrix operations in numba functions
  • 0.6.1 added scaling and rounding
  • 0.6.2 removed seedpy dependency
  • 0.6.3 renamed shots to size in BinarySample; cleaned up sampling, simplified type hints
  • 0.6.4 added probabilistic functions to qubo class
  • 0.6.5 complete empirical prob. vector can be returned from BinarySample
  • 0.6.6 fixed spectral gap implementation
  • 0.6.7 moved brute_force to new sub-module solving; added some approximate solving methods
  • 0.6.8 added bitvec sub-module; dynamic_range now uses bits by default, changed bits=False to decibel=False; removed scipy from requirements