Quantum-Inspired Algorithmic Randomness Generation

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the potential of harnessing quantum mechanical principles, specifically superposition and entanglement, to generate truly random numbers. Current classical random number generators rely on entropy sources that, while statistically random, are fundamentally predictable. We propose a novel algorithmic approach termed "Quantum-Inspired Algorithmic Randomness Generation" (Q-IARG) that utilizes controlled quantum decoherence and measurement processes to generate high-entropy random numbers. The core concept involves mapping the probabilistic outcomes of these processes—defined by equations and mathematical models—to a sequence of random numbers. This approach moves beyond merely simulating quantum phenomena and aims for a practically demonstrable improvement in randomness quality. The system is envisioned as a hybrid hardware/software solution, offering a pathway to surpass the limitations of traditional entropy sources. The system's effectiveness will be assessed through the analysis of the statistical properties of the generated sequences, focusing on metrics such as entropy, autocorrelation, and uniformity. This work provides a foundational framework for exploring quantum-inspired randomness generation and its potential applications.

DOI: 10.5281/zenodo.22315986

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