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arXiv:2605.30977

Software Platform for Hybrid Pseudo-Random Sequence Generation and Predictability Analysis Based on LFSR and Mersenne Twister

Ali Abdolrahimi Zarnagh, Ali Motazedifard

quant-phcs.CRSubmitted May 29, 2026• Announced Jun 1, 2026
arXivHTMLPDF

Abstract

Generating reliable random and pseudo-random sequences is important in many electronic and signal processing systems, such as secure communications, radar, spread-spectrum methods, and autonomous platforms. Although true and quantum random number generators provide stronger unpredictability, classical pseudo-random number generators, including Linear Feedback Shift Registers (LFSRs) and the Mersenne Twister (MT), are still widely used because they are efficient and easy to implement. This work introduces a user-friendly software platform for generating, analyzing, and evaluating the predictability of pseudo-random bit sequences. The software supports two main functions: generating sequences using classical PRNGs and hybrid combinations, and analyzing input sequences through statistical measures and data-driven methods. In particular, hybrid LFSR-MT structures are studied to examine how they affect sequence complexity and resistance to prediction. The platform also includes machine-learning and deep-learning tools to investigate when deterministic PRNGs may remain partially predictable, even when their structure becomes more complex. The results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability, which supports the use of quantum random sequences in security-critical applications. A comparative study between classical LFSR-MT sequences and quantum random sequences shows that quantum randomness offers higher unpredictability due to its non-deterministic physical origin. The potential use of quantum random sequences in jamming applications is also discussed, highlighting their improved robustness against prediction-based attacks. Overall, the proposed software provides a practical tool for analyzing, comparing, and benchmarking random sequence generators in modern electronic, sensing, and quantum-enabled communication systems.

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Generating reliable random and pseudo-random sequences is important in many electronic and signal processing systems, such as secure communications, radar, spread-spectrum methods, and autonomous platforms. Although true and quantum random number generators provide stronger unpredictability, classical pseudo-random number generators, including Linear Feedback Shift Registers (LFSRs) and the Mersenne Twister (MT), are still widely used because they are efficient and easy to implement. This work introduces a user-friendly software platform for generating, analyzing, and evaluating the predictability of pseudo-random bit sequences. The software supports two main functions: generating sequences using classical PRNGs and hybrid combinations, and analyzing input sequences through statistical measures and data-driven methods. In particular, hybrid LFSR-MT structures are studied to examine how they affect sequence complexity and resistance to prediction. The platform also includes machine-learning and deep-learning tools to investigate when deterministic PRNGs may remain partially predictable, even when their structure becomes more complex. The results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability, which supports the use of quantum random sequences in security-critical applications. A comparative study between classical LFSR-MT sequences and quantum random sequences shows that quantum randomness offers higher unpredictability due to its non-deterministic physical origin. The potential use of quantum random sequences in jamming applications is also discussed, highlighting their improved robustness against prediction-based attacks. Overall, the proposed software provides a practical tool for analyzing, comparing, and benchmarking random sequence generators in modern electronic, sensing, and quantum-enabled communication systems.
Topicality. Reliable random number generation is one of the key requirements for modern cryptographic systems and cybersecurity applications. Traditional pseudo-random number generators are based on deterministic algorithms and may exhibit limited entropy and predictability, which can negatively affect information security. The subject of study in the article is methods of quantum random number generation and approaches to evaluating the quality of generated random sequences. The purpose of the article is to analyze modern methods of quantum random number generation, develop a software tool for obtaining quantum random numbers based on the ANU Quantum Random Number Generator service, perform statistical testing of the generated sequences, and analyze the obtained results. The following results were obtained. Modern quantum random number generation systems were analyzed and compared. A software tool was implemented in Python for automatic retrieval of quantum random numbers through the ANU QRNG API. The generated binary sequences were evaluated using Frequency Test and Block Frequency Test from the NIST SP 800-22 statistical test suite. The obtained p-values were 0.977181 and 0.200659 respectively, confirming successful completion of both tests. Additionally, visual analysis of the generated sequences revealed no noticeable patterns or repetitive structures. Conclusion. The obtained results confirm the high quality of quantum random sequences generated using ANU QRNG and demonstrate the applicability of the developed software tool for cryptographic and cybersecurity applications requiring high-quality randomness.
Linear-feedback shift register (LFSR) based pseudo-random number generator (PRNG) has applications in a plethora of fields. The issue of being linear is generally circumvented by introducing non-linearities as per the required applications, with some being adhoc but fulfilling the purpose while others with a theoretical proof. The goal of this study is to develop a sufficiently ``random" resource for Quantum Key Distribution (QKD) applications with a low computational cost. However, as a byproduct, we have also studied the effect of introducing minimum non-linearity with experimental verification. Starting from the numerical implementation to generate a random sequence, we have implemented a XOR of two LFSR sequences on a low-cost FPGA evaluation board with one of the direct use cases in QKD protocols. Such rigorously tested random numbers could also be used like artificial neural networks or testing of circuits for integrated chips and directly for encryption for wireless technologies.
Generating high-quality random numbers is essential for cryptography, statistical simulations, and cybersecurity. While pseudo-random number generators (PRNGs) are computationally efficient, their deterministic nature introduces potential security vulnerabilities. Recent research has explored chaotic systems as entropy sources due to their sensitivity to initial conditions-a key property for randomness generation. An emerging direction involves leveraging chaos in quantum systems. This paper proposes a quantum-inspired PRNG based on the Quantum Kicked Rotor (QKR), a well-known model in quantum chaos exhibiting complex dynamics such as dynamical localization. The method simulates the QKR and extracts pseudo-random sequences through momentum basis measurements. The generated sequences are evaluated for statistical quality, including the NIST 800-22 standard, which passed all 15 test. The sequences exhibit strong non-periodicity, a critical property for secure cryptographic applications. The proposed approach combines the unpredictability of quantum chaos with the scalability of classical simulation, offering a practical and efficient alternative for secure random number generation.
This paper explores the design of cryptographic algorithms leveraging the inherent randomness of quantum random number generators (QRNGs). Traditional cryptographic algorithms often rely on pseudo-random number generators (PRNGs), which are deterministic and therefore vulnerable to attacks if their internal state is compromised. QRNGs, on the other hand, derive their randomness from quantum mechanical phenomena, offering a fundamentally unpredictable source of numbers. This research investigates the application of QRNGs to enhance cryptographic security, focusing on constructing algorithms that exploit this unique property. The core claim is that utilizing QRNG characteristics allows for the design of more secure cryptographic algorithms. The proposed mechanism involves capitalizing on the non-deterministic nature of QRNGs to build algorithms with heightened security levels. This approach presents a novel security guarantee method by incorporating true randomness into cryptographic processes. We examine specific algorithm designs and analyze their security implications compared to traditional PRNG-based approaches. The paper concludes with a discussion on the challenges and future directions of utilizing QRNGs in cryptography.
This paper investigates the execution of classical pseudorandom number generators (PRNGs) on quantum hardware, focusing on their behavior within the qBraid quantum computing platform. Although classical PRNGs such as the Mersenne Twister, LCG, and Blum-Blum-Shub are well-established in cryptographic and simulation applications, their operation in quantum environments has remained largely unexamined. This paper conducts a comprehensive analysis of their statistical and cryptographic performance under quantum-specific constraints, including qubit decoherence, gate noise, and circuit-level errors. The selected PRNGs are modeled and visualized using quantum circuit abstractions, then evaluated for output randomness using NIST SP 800-22 tests and distributional fidelity against uniform and gamma laws. The results reveal varying degrees of robustness among generators when subjected to quantum noise, offering insights into their suitability for hybrid quantum-classical systems. Additionally, their effectiveness is tested in an applied scenario of image encryption, demonstrating the real-world implications of quantum-induced randomness deviations. These findings lay the foundation for designing quantum-aware pseudorandom number generation strategies essential for secure and efficient quantum era applications.
One of the solutions to the problem of secure communication is the Quantum Random Number Generators (QRNGs) which generate true randomness based on quantum mechanical principles.QRNGs are different from conventional pseudo random number generator in that they use quantum uncertainty to generate outputs which are very unpredictable.This work attempts to examine different QRNG architectures, study the possible integration of cryptographic frameworks with these architectures and highlight potential vulnerabilities in the integration of cryptographic frameworks with QRNG architectures.Post-processing techniques, i.e.Zero-Phase Component Analysis (ZPCA) and neural network based techniques are also studied to increase entropy and suppress environmental noise in QRNG systems.Key findings show that system jitter models based QRNG architectures are more scalable and less expensive for use in real world applications.Additionally, incorporating cryptographic protocols with the QRNGs endows them with the ability to resist quantum threats, and hence, provide better key distribution in the secure IoT communication frameworks.However, as security problems such as side channel attacks and hardware manipulation are still big issues.It also points out some of the emerging research trends including hybrid QRNG architectures, i.e., QRNG with classical PRNG to increase the entropy rate.As such, the proposed methodologies attempt to enhance QRNG scalability, security, and energy efficiency in cloud infrastructures, in mobile systems and in critical security environments.The advancement of these technologies will assume a crucial function in the advancement of quantum-safe frames for current advanced computerized foundation.