This study investigates the capability of a field-programmable gate array (FPGA)-based quantum bit (QUBIT) emulator to replicate the quantum superposition state with randomness evaluation serving as the primary verification method. The QUBIT device is built on the FPGA, an integrated circuit that is a massively parallel array of independent logic elements. Using two QUBIT devices, a simple random number generator is created and statistical tests are used to verify randomness. These tests provide a quantitative measure of randomness quality, reflecting the emulator effectiveness at mimicking quantum superposition. The results offer insights into the limitations and potential of using the emulator (QUBIT) as an alternative to a physical quantum system. Randomness of generated numbers (bit strings) was tested with the NIST Statistical Test Suite (SP 800-22), widely regarded as the standard for randomness evaluation. In total, 106 bits were generated and tested with different block lengths: 10 blocks of 105 bits and 100 blocks of 104 bits. The observed deviations indicate that while the emulator can serve as an educational tool, its statistical properties currently limit its applicability in cryptographic contexts.
This study investigates the capability of a field-programmable gate array (FPGA)-based quantum bit (QUBIT) emulator to replicate the quantum superposition state with randomness evaluation serving as the primary verification method. The QUBIT device is built on the FPGA, an integrated circuit that is a massively parallel array of independent logic elements. Using two QUBIT devices, a simple random number generator is created and statistical tests are used to verify randomness. These tests provide a quantitative measure of randomness quality, reflecting the emulator effectiveness at mimicking quantum superposition. The results offer insights into the limitations and potential of using the emulator (QUBIT) as an alternative to a physical quantum system. Randomness of generated numbers (bit strings) was tested with the NIST Statistical Test Suite (SP 800-22), widely regarded as the standard for randomness evaluation. In total, 106 bits were generated and tested with different block lengths: 10 blocks of 105 bits and 100 blocks of 104 bits. The observed deviations indicate that while the emulator can serve as an educational tool, its statistical properties currently limit its applicability in cryptographic contexts.
The rapidly developing field of quantum computing and the ongoing lack of widely available quantum computers create the need for scientists to build their simulators. However, mathematical simulation of such circuits usually ignores many aspects and problems found in real quantum systems. In this article, the authors describe a quantum bit emulator based on FPGA integrated circuits. In this case, FPGA technology provides real-time massive parallelism of the modeled physical phenomena. The modeled QUBIT is represented using a Bloch sphere. Its quantum state is set and modified only by precise pulses of an electrical signal, and with the help of similar pulses, it manifests its current state in real time. The constructed QUBIT was additionally equipped with decoherence mechanisms and with circuits that intentionally respond to internal and external noises that distort its current quantum state. This article presents and discusses how such a physically built emulator works.
The rapidly developing field of quantum computing and the ongoing lack of widely available quantum computers create the need for scientists to build their simulators. However, mathematical simulation of such circuits usually ignores many aspects and problems found in real quantum systems. In this article, the authors describe a quantum bit emulator based on FPGA integrated circuits. In this case, FPGA technology provides real-time massive parallelism of the modeled physical phenomena. The modeled QUBIT is represented using a Bloch sphere. Its quantum state is set and modified only by precise pulses of an electrical signal, and with the help of similar pulses, it manifests its current state in real time. The constructed QUBIT was additionally equipped with decoherence mechanisms and with circuits that intentionally respond to internal and external noises that distort its current quantum state. This article presents and discusses how such a physically built emulator works.
We present a scalable experimental framework for validating quantum-generated bitstreams using the NIST SP 800-22 statistical test suite. Quantum randomness was simulated using Qiskit’s aer_simulator, generating 10 independent sequences of 10,000,000 bits each. These bitstreams were evaluated across 15 statistical categories including Monobit, Block Frequency, FFT, Linear Complexity, and Excursion-based tests. All but one test passed the NIST-recommended thresholds for both pass rates and p-value distributions. Specifically, the Random Excursion (Standard) test narrowly missed the threshold with 52 out of 56 sequences passing. Despite this, the overall results confirm the high entropy and statistical randomness of the quantum-generated sequences. Our framework also incorporates a batch-based architecture that enforces streak continuity across sequence boundaries and uses SHA-256 hashing for integrity verification. This infrastructure supports reproducibility and lays the foundation for future exploration of probabilistic thresholds in ultra-large-scale quantum bitstreams. In particular, we plan to generate and analyze up to one trillion quantum bits to investigate whether there exists a natural limit to the length of repeated stochastic patterns (streaks) in true randomness.
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.
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.
Quantum devices offer a highly useful function - that is generating random numbers in a non-deterministic way since the measurement of a quantum state is not deterministic. This means that quantum devices can be constructed that generate qubits in a uniform superposition and then measure the state of those qubits. If the preparation of the qubits in a uniform superposition is unbiased, then quantum computers can be used to create high entropy, secure random numbers. Typically, preparing and measuring such quantum systems requires more time compared to classical pseudo random number generators (PRNGs) which are inherently deterministic algorithms. Therefore, the typical use of quantum random number generators (QRNGs) is to provide high entropy secure seeds for PRNGs. Quantum annealing (QA) is a type of analog quantum computation that is a relaxed form of adiabatic quantum computation and uses quantum fluctuations in order to search for ground state solutions of a programmable Ising model. Here we present extensive experimental random number results from a D-Wave 2000Q quantum annealer, totaling over 20 billion bits of QA measurements, which is significantly larger than previous D-Wave QA random number generator studies. Current quantum annealers are susceptible to noise from environmental sources and calibration errors, and are not in general unbiased samplers. Therefore, it is of interest to quantify whether noisy quantum annealers can effectively function as an unbiased QRNG. The amount of data that was collected from the quantum annealer allows a comprehensive analysis of the random bits to be performed using the NIST SP 800-22 Rev 1a testsuite, as well as min-entropy estimates from NIST SP 800-90B. The randomness tests show that the generated random bits from the D-Wave 2000Q are biased, and not unpredictable random bit sequences. With no server-side sampling post-processing, the 1 microsecond annealing time measurements had a min-entropy of 0.824.