Quantum Adiabatic Generation of Human-Like Passwords

Generative artificial intelligence (GenAI) for natural language processing (NLP) is the predominant AI-technology to-date. An imporant perspective for quantum computing (QC) is the question whether QC has the potential to reduce the vast resource requirements for training und operating GenAI models. While large-scale generative NLP tasks are currently out-of-reach for practical quantum computers, generating passwords is not. Classical password guessing approaches 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 password guessing. More precisely, we study different encodings of token strings and propose novel approaches based on the quadratic unconstrained binary optimization and the unit-disc maximum independent set problems. Our approach allows us to estimate the token distribution from data and adiabatically prepare a quantum state from which we eventually measure the generated passwords. Our results show that relatively small samples of 128 passwords, generated on a 256-qubit neutral-atom quantum computer contain human-like passwords like Tunas200992 or teedem28iglove.