Can Quantum Computing Improve Uniform Random Sampling of Large Configuration Spaces? (Preprint)

TL;DR

This paper explores how quantum computing can enhance the uniformity and randomness of sampling large configuration spaces, potentially improving software testing and verification processes.

Contribution

It proposes a quantum method to encode and sample configuration spaces uniformly, analyzing its scalability and limitations with current and future quantum hardware.

Findings

Quantum encoding achieves uniform sampling over configuration spaces.

The method's scalability varies with feature model complexity.

Limitations are discussed concerning current quantum hardware capabilities.

Abstract

A software product line models the variability of highly configurable systems. Complete exploration of all valid configurations (the configuration space) is infeasible as it grows exponentially with the number of features in the worst case. In practice, few representative configurations are sampled instead, which may be used for software testing or hardware verification. Pseudo-randomness of modern computers introduces statistical bias into these samples. Quantum computing enables truly random, uniform configuration sampling based on inherently random quantum physical effects. We propose a method to encode the entire configuration space in a superposition and then measure one random sample. We show the method's uniformity over multiple samples and investigate its scale for different feature models. We discuss the possibilities and limitations of quantum computing for uniform random sampling regarding current and future quantum hardware.