Abstract Quantum noise, typically considered an obstacle in quantum computing, provides novel prospects in random number generation. This research investigates the possibility of several forms of quantum noise as significant resources for producing high-quality randomness. The study employed simulations of random quantum circuits to examine the effects of bit-flips, phase-flips, and depolarizing noise at different intensities. We conducted the simulations in a controlled setting using the Qiskit Aer simulator, which we selected for its ability to accurately mimic noise effects and replicate the behaviour of actual quantum gear. The research indicated that bit-flip and depolarizing noise consistently produced more significant amounts of unpredictability than phase-flip and noiseless circuits. Applying the produced binary sequences to the NIST Statistical Test Suite corroborated the discovery, confirming their superior quality and appropriateness for applications requiring random numbers. The study suggests that we can efficiently use quantum noise to create safe randomness, which offers significant benefits for encryption, simulations, and other domains that demand genuine unpredictability.
Abstract Quantum noise, typically considered an obstacle in quantum computing, provides novel prospects in random number generation. This research investigates the possibility of several forms of quantum noise as significant resources for producing high-quality randomness. The study employed simulations of random quantum circuits to examine the effects of bit-flips, phase-flips, and depolarizing noise at different intensities. We conducted the simulations in a controlled setting using the Qiskit Aer simulator, which we selected for its ability to accurately mimic noise effects and replicate the behaviour of actual quantum gear. The research indicated that bit-flip and depolarizing noise consistently produced more significant amounts of unpredictability than phase-flip and noiseless circuits. Applying the produced binary sequences to the NIST Statistical Test Suite corroborated the discovery, confirming their superior quality and appropriateness for applications requiring random numbers. The study suggests that we can efficiently use quantum noise to create safe randomness, which offers significant benefits for encryption, simulations, and other domains that demand genuine unpredictability.
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.
This study examines how noise affects the reliability of quantum circuits through two complementary approaches: a controlled simulation using Qiskit Aer and an execution study on real IBM Quantum hardware. In simulation, five noise models (bit-flip, phase-flip, depolarizing, readout, and thermal-relaxation) were applied to a parameterized benchmark circuit across varying qubit counts, circuit depths, and noise strengths, producing 6,000 experimental observations. Success probability declined substantially as qubit count and circuit depth increased, with bit-flip, phase-flip, and depolarizing noise degrading performance far more severely (mean success probabilities of 0.597, 0.580, and 0.607) than readout noise (0.917) or thermal-relaxation noise (0.982). The same benchmark circuit was then executed on the ibm_marrakesh processor across 150 hardware runs (150,000 shots). Hardware performance remained consistently high (mean success probability 0.992), declining with qubit count (0.998 at two qubits to 0.986 at six) but showing little sensitivity to circuit depth. These results indicate that controlled noise mechanisms and real hardware degrade circuit performance through different dynamics, with implications for interpreting simulated noise models relative to physical device behavior.
True randomness is a basic necessity of secure cryptography. This secures the reliability of encryption keys, digital signatures and authentication mechanisms. Classical random number generators based on deterministic algorithms can introduce patterns and decrease reliability. This paper introduces a Quantum True Random Number Generator (QTRNG) takes advantage of the intrinsic unpredictability of quantum mechanics. The proposed quantum circuit usesRY(π/2)andRZ(π/2)rotations to create superposition states like Hadamard operation. These gates control the phase and orientation of qubits on the Bloch sphere yields measurement outcomes are inherently random. The resulting distributions are unbiased and balanced offers a source of true randomness. The quality of the generated bitstrings is verified using statistical tests such as the NIST SP 800-22, SP 800-90 test suites and autocorrelation tests. The measurements show high entropy, uniform distribution and low bias satisfy the basic requirements for cryptographic security. Phase-based quantum operations provide an efficient for producing high-quality quantum randomness facilitates the construction of effective quantum-secure cryptographic systems.
This paper explores the generation of high-quality random numbers utilizing the principles of quantum chaos. Traditional random number generators often exhibit limitations in terms of randomness and predictability, posing challenges for applications in secure communication and cryptography. This research investigates the potential of quantum chaos to overcome these limitations, proposing a novel approach based on quantum chaotic algorithms. The core of this approach lies in the generation of sequences exhibiting chaotic properties, subsequently analyzed to extract random numbers. The generated numbers are characterized by their enhanced randomness and unpredictability, offering a significant advancement over conventional methods. This work outlines the theoretical framework, the underlying mathematical principles, and the potential applications of quantum chaotic random number generation. The primary goal is to demonstrate a method capable of producing truly random sequences, crucial for secure systems.
Generation of random numbers is a central problem for many applications in the field of information processing, including, e.g., cryptography, in classical and quantum regime, but also mathematical modeling, Monte Carlo methods, gambling and many others. Both, the quality of the randomness and efficiency of the random numbers generation process are crucial for the most of these applications. Software produced pseudorandom bit sequences, though sufficiently quick, do not fulfill required randomness quality demands. Hence, the physical hardware methods are intensively developed to generate truly random number sequences for information processing and electronic security application. In the present paper we discuss the idea of the quantum random number generators. We also present a variety of tests utilized to assess the quality of randomness of generated bit sequences. In the experimental part we apply such tests to assess and compare two quantum random number generators, PQ4000KSI (of company ComScire US) and JUR01 (constructed in Wroclaw University of Science and Technology upon the project of The National Center for Research and Development) as well as a pseudorandom generator from the Mathematica Wolfram package. Finally, we present our new prototype of fully operative miniaturized quantum random generator JUR02 producing a random bit sequence with velocity of 1 Mb/s, which successfully passed standard tests of randomness quality (like NIST and Dieharder tests). We also shortly discuss our former concept of an entanglement-based quantum random number generator protocol with unconditionally secure public randomness verification.
Quantum computing promises unparalleled computational power, but its practical implementation is hampered by quantum noise, which affects the reliability of quantum circuits. This paper addresses the critical challenge of noise classification in NISQ ‘Noisy Intermediate-Scale Quantum’ algorithms, focusing on bit flip, phase flip, and depolarising noise. We introduce a novel balanced quantum noise dataset generated through random quantum circuits, ensuring comprehensive coverage of various noise types and intensities. Additionally, we develop a hybrid quantum-classical machine learning model that achieves over 85% accuracy in classifying bit flip and depolarising noise, and perfect accuracy in detecting phase flip noise under our simulation conditions. Our approach builds on a dual quantum architecture, utilising IBM Qiskit for dataset generation and Pennylane for model training. These simulation results indicate potential improvements for noise-aware modelling and error mitigation; however, we emphasise that translating this performance to physical devices requires further validation due to hardware-specific decoherence and calibration effects.