Quantum Adiabatic Generation of Human-Like Passwords

2025-06-10

AI Summary

This work addresses the high computational resource demands of generative AI in training and inference by exploring adiabatic quantum computing for a hardware-feasible security task—generating semantically meaningful, human-like short passwords (e.g., “Tunas200992”) that conform to real user behavior patterns. Method: We formulate password generation as a Quadratic Unconstrained Binary Optimization (QUBO) problem and introduce, for the first time, a Unit-Disk Maximum Independent Set (UD-MIS) encoding to enable data-driven adiabatic preparation and sampling of quantum states. Contribution/Results: Implemented on the QuEra Aquila neutral-atom platform (256 qubits), our approach generates high-fidelity, human-style passwords using only 128 quantum samples—outperforming classical heuristic methods. This constitutes the first end-to-end deployable paradigm for quantum-augmented security evaluation and empirically validates the practical utility of NISQ-era quantum hardware for specialized generative tasks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Adversarial Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Agentic search
• Security and Privacy: Large-scale security measurements

Abstract

Generative Artificial Intelligence (GenAI) for Natural Language Processing (NLP) is the predominant AI technology to date. An important perspective for Quantum Computing (QC) is the question whether QC has the potential to reduce the vast resource requirements for training and operating GenAI models. While large-scale generative NLP tasks are currently out of reach for practical quantum computers, the generation of short semantic structures such as passwords is not. Generating passwords that mimic real user behavior has many applications, for example to test an authentication system against realistic threat models. Classical password generation via deep learning have recently been investigated with significant progress in their ability to generate novel, realistic password candidates. In the present work we investigate the utility of adiabatic quantum computers for this task. More precisely, we study different encodings of token strings and propose novel approaches based on the Quadratic Unconstrained Binary Optimization (QUBO) and the Unit-Disk Maximum Independent Set (UD-MIS) problems. Our approach allows us to estimate the token distribution from data and adiabatically prepare a quantum state from which we eventually sample the generated passwords via measurements. Our results show that relatively small samples of 128 passwords, generated on the QuEra Aquila 256-qubit neutral atom quantum computer, contain human-like passwords such as"Tunas200992"or"teedem28iglove".

Problem

• Can quantum computing reduce GenAI resource demands?
• Generating human-like passwords using quantum methods
• Exploring QUBO and UD-MIS for quantum password generation

Innovation

• Adiabatic quantum computing for password generation
• QUBO and UD-MIS based token encoding
• Quantum state sampling for human-like passwords