Quantum noise-driven randomness for password generation: Analysis via simulation and FakeProvider

Abstract

This study introduces a quantum-enhanced approach to password generation using two distinct quantum randomness sources: classical measurement of superposition states and depolarizing noise. These sources overcome the shortcomings of classical pseudo-random number generators. Notably, this work pioneers integrating depolarizing noise as a controlled entropy source alongside superposition, offering a novel alternative to conventional quantum randomness methods. Quantum circuits, implemented via Qiskit, were executed on AerSimulator (idealized simulation) and FakeProvider (hardware-inspired noise model) backends, producing binary sequences through XOR and Concatenation modes. These sequences were converted into passwords of lengths 16 and 25 across datasets of 10K, 100K, and 500K, using Binary Character Mapping and Randomized reseeding methods. Binary entropy’s randomness was verified by the NIST Statistical Test Suite, with all p-values above 0.01. Generated password strength was evaluated through Clustering (Levenshtein distance), Entropy (3.84 to 4.40 bits per character), Hamming Distance, Uniqueness (100 percent), and ZXCVBN scoring (Score 4). Results from both backend-based password datasets were nearly identical, confirming simulated and hardware-based quantum randomness as a reliable and scalable alternative to systems. This work positions quantum noise-driven password generation as a robust cryptographic tool with the potential for real-world security applications.This research investigates the effectiveness of quantum noise, derived from superposition states and depolarizing channels, as a high-entropy source for generating cryptographically secure passwords. Quantum circuits implemented on the AerSimulator and FakeProvider backends produce random binary sequences, which are processed through chaotic mixing and rejection sampling to ensure uniform randomness. These sequences are rigorously evaluated using Shannon entropy, min-entropy and the NIST statistical test suite, alongside password strength assessments via tools like ZXCVBN. Results indicate exceptional randomness, with an average Shannon entropy of 3.84 ± 0.4 bits per character and 100% uniqueness across datasets of 20,000 passwords. The FakeProvider backend slightly outperforms the AerSimulator, achieving full compliance with NIST tests, while both produce passwords with top-tier strength scores (ZXCVBN Score 4). Comparative analyses demonstrate that quantum noise surpasses classical pseudo-random number generators in entropy density and resistance to brute-force and dictionary-based attacks. This study validates quantum noise as a superior entropy source, offering significant potential for enhancing password security in cryptographic applications and paving the way for quantum-enhanced cybersecurity solutions.