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arXiv:2507.14600

A Hybrid Classical-Quantum Rainbow Table Attack on Human Passwords

MA. Khajeian

Published Jul 19, 2025• 0 citations
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Abstract

Long, human-generated passwords pose significant challenges to both classical and quantum attacks due to their irregular structure and large search space. In this work, we propose an enhanced classical-quantum hybrid attack specifically designed for this scenario. Our approach constructs rainbow tables using dictionary-based password generation augmented with transformation rules that better capture real-world user behavior. These tables are organized into buckets, enabling faster lookup and reduced space complexity. For the search within each bucket, we employ a distributed exact variant of Grover's algorithm. This method provides deterministic success and significantly lower circuit depth, enhancing robustness against noise-particularly depolarizing errors common in near-term quantum devices. Overall, our hybrid framework improves the efficiency and practicality of password recovery for long, human-readable passwords in realistic adversarial settings.

DOI
10.48550/arxiv.2507.14600

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Long, human-generated passwords pose significant challenges to both classical and quantum attacks due to their irregular structure and large search space. In this work, we propose an enhanced classical-quantum hybrid attack specifically designed for this scenario. Our approach constructs rainbow tables using dictionary-based password generation augmented with transformation rules that better capture real-world user behavior. These tables are organized into buckets, enabling faster lookup and reduced space complexity. For the search within each bucket, we employ a distributed exact variant of Grover's algorithm. This method provides deterministic success and significantly lower circuit depth, enhancing robustness against noise-particularly depolarizing errors common in near-term quantum devices. Overall, our hybrid framework improves the efficiency and practicality of password recovery for long, human-readable passwords in realistic adversarial settings.
In the booming field of quantum computing, Grover's Algorithm emerges as a pivotal quantum search algorithm, theoretically capable of outperforming classical brute-force search methods by exploiting the principles of superposition and quantum parallelism. However, the practical application of Grover's Algorithm to crack complex passwords is limited by the nascent stage of quantum technology, which is currently challenged by limitations in qubit count and coherence times. This paper proposes a novel distributed quantum computing framework that aims to circumvent these limitations by partitioning the search space for a complex password among an array of quantum processors. Each processor executes Grover's Algorithm on a fraction of the total search space, thereby reducing the complexity and quantum resource requirements for each individual computation. The partial results are then transmitted to a central classical computer, which synthesizes them into the final password. This distributed approach not only makes the task feasible for (with) present-day quantum systems but also presents a scalable model that could adapt to the rapid advancements in quantum technologies. By dissecting the complexity of the quantum brute-force problem into smaller, more manageable units, this method holds the potential to significantly reduce the time complexity of password recovery processes and thus presents a profound progress (implications) for the fields of cryptography and cybersecurity.
Passwords remain a universal mechanism for user authentication, yet they are troubled by weak choices and vulnerabilities. The arrival of quantum computing threatens to worsen these issues by enabling faster password cracking. Conventional wisdom suggests using longer or more complex passwords to counter quantum attacks, but this approach can be impractical.In this paper, we propose a novel quantum-resistant password generation framework that leverages a Quantum Neural Network (QNN) to create highly secure passwords. Our approach combines quantum machine learning with entropy-based rigorous analysis to ensure that generated passwords are resilient even against adversaries equipped with quantum computing capabilities. We design and train a QNN using large-scale password datasets (including RockYou, Have I Been Pwned, and others) to learn the distribution of common passwords and then generate passwords that avoid these patterns. To rigorously quantify security, we replace simplistic entropy estimations with a von Neumann entropy analysis, measuring the quantum entropy of the password generation process. The experimental results demonstrate that our QNN-generated passwords achieve significantly higher entropy and resistance to guessing attacks compared to conventional methods. This research highlights how quantum computing can be harnessed not only as a threat but as a tool to enhance security.
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The digitization of our daily lives, driven by the Internet of Things (IoT) and other applications, has heightened the importance of data security. This paper explores the impact of quantum computing on password security, specifically focusing on Grover’s algorithm’s application to password cracking. We compare classical brute-force methods with quantum search techniques, utilizing real-world password data from significant breaches such as LinkedIn, 000webhost, and RockYou. Our study involves the development of a tool that tests password robustness using both classical and quantum simulations, and also generates secure passwords based on quantum randomness. The results obtained provide an insight into the application of quantum simulators for the implementation of quantum algorithms. They also illustrate how quantum random number generation helps to improve password security.
Current quantum neural networks suffer from extreme sensitivity to both adversarial perturbations and hardware noise, creating a significant barrier to real-world deployment. Existing robustness techniques typically sacrifice clean accuracy or require prohibitive computational resources. We propose a hybrid quantum-classical Differentiable Quantum Architecture Search (DQAS) framework that addresses these limitations by jointly optimizing circuit structure and robustness through gradient-based methods. Our approach enhances traditional DQAS with a lightweight Classical Noise Layer applied before quantum processing, enabling simultaneous optimization of gate selection and noise parameters. This design preserves the quantum circuit's integrity while introducing trainable perturbations that enhance robustness without compromising standard performance. Experimental validation on MNIST, FashionMNIST, and CIFAR datasets shows consistent improvements in both clean and adversarial accuracy compared to existing quantum architecture search methods. Under various attack scenarios, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Basic Iterative Method (BIM), and Momentum Iterative Method (MIM), and under realistic quantum noise conditions, our hybrid framework maintains superior performance. Testing on actual quantum hardware confirms the practical viability of discovered architectures. These results demonstrate that strategic classical preprocessing combined with differentiable quantum architecture optimization can significantly enhance quantum neural network robustness while maintaining computational efficiency.
Grover's Algorithm is a prominent quantum search algorithm in the rapidly advancing quantum computing study. By exploiting superposition and quantum parallelism, it has the potential to outperform classical brute-force approaches. Nevertheless, the current practical application of quantum technology in decrypting intricate passwords is restricted by its early stage, namely the restricted qubit quantities and short coherence durations. This work presents an innovative distributed quantum computing paradigm that addresses these difficulties by partitioning the password search space among several quantum processors. Each processor executes Grover's Algorithm on a subset of the search space, minimizing the computational complexity and quantum resource requirements for each individual processor. The partial findings are subsequently transmitted to a central classical computer, which amalgamates them to obtain the ultimate password. This distributed methodology not only allows for practicality with current quantum systems but also provides a scalable paradigm that can adapt to the progress of quantum computer technology. By decomposing the quantum brute-force search problem into smaller, more controllable elements, this approach can significantly decrease the time complexity of password retrieval, which has significant consequences for cryptography and cybersecurity.