We evaluated the quality of conventional quantum True Random Number Generator (TRNG) using superconducting qubits from IBM. Our analysis indicated that the 1/0 ratio is severely affected by the noise. We proposed swapping of readout qubit and parameter optimization to fix readout and gate errors. We validate our proposals by running experiments of IBM's su-perconducitng qubit based quantum computer. Experiments show deviation of ≈ 35% from ideal 1/0 ratio for baseline implementation. After the application of our proposed techniques the ratio improves and reaches close to ideal value of 1. The random bits generated through our techniques passes NIST statistical tests as well.
We evaluated the quality of conventional quantum True Random Number Generator (TRNG) using superconducting qubits from IBM. Our analysis indicated that the 1/0 ratio is severely affected by the noise. We proposed swapping of readout qubit and parameter optimization to fix readout and gate errors. We validate our proposals by running experiments of IBM's su-perconducitng qubit based quantum computer. Experiments show deviation of ≈ 35% from ideal 1/0 ratio for baseline implementation. After the application of our proposed techniques the ratio improves and reaches close to ideal value of 1. The random bits generated through our techniques passes NIST statistical tests as well.
Quantum computer (QC) can be used as a true random number generator (TRNG). However, various noise sources introduce a bias in the generated number which affects the randomness. In this work, we analyze the impact of noise sources e.g., gate error, decoherence, and readout error in QC-based TRNG by running a set of error calibration and quantum tomography experiments. We employ a hybrid quantum-classical gate parameter optimization routine to find an optimal gate parameter. The optimal parameter compensates for error-induced bias and improves the quality of the random number by exploiting even the worst quality qubits. However, searching the optimal parameter in a hybrid setup requires time-consuming iterations between classical and quantum machines. We propose a machine learning model to predict optimal quantum gate parameters based on the qubit error specifications. We validate our approach using experimental results from IBM's publicly accessible quantum computers and the NIST statistical test suite. The proposed method can correct bias in any worst-case qubit by up to 88.57% in real quantum hardware.
Random number generation is fundamental for many modern applications including cryptography, simulations and machine learning. Traditional pseudo-random numbers may offer statistical unpredictability, but are ultimately deterministic. On the other hand, True Random Number Generation (TRNG) offers true randomness. One way of obtaining such randomness are quantum systems, including quantum computers. As such the use of quantum computers for TRNG has received considerable attention in recent years. However, existing studies almost exclusively consider IBM quantum computers, often stop at using simulations and usually test only a handful of different TRNG quantum circuits. In this paper, we address those issues by presenting a study of TRNG circuits on Odra 5 a real-life quantum computer installed at Wrocław University of Science and Technology. It is also the first study to utilize the IQM superconducting architecture. Since Odra 5 is available on-premises it allows for much more comprehensive study of various TRNG circuits. In particular, we consider 5 types of TRNG circuits with 105 circuit subvariants in total. Each circuit is used to generate 1 million bits. We then perform an analysis of the quality of the obtained random sequences using the NIST SP 800-22 and NIST SP 800-90B test suites. We also provide a comprehensive review of existing literature on quantum computer-based TRNGs.
Random numbers are used in various domains, including quantum communication and cryptography applications such as key generation and authentication. Quantum mechanics has the intrinsic ability to generate truly random numbers, making it an ideal alternative for scientific applications that require randomness. Quantum wireless communication is proposed as a new means of communication that is safe, secure, and efficient. The simplicity of our source rotation gate, combined with its independently verifiable solitary unpredictability, is essential to obtain quantum random number generators without spending any money. In this chapter, we explore and study the 24-qubit random number. The worst-case entropy value for such randomly produced integers is 0.999445, with the min-entropy of such numbers being 0.0008. Additionally, steering restart tests were used to validate later verification of true randomness, and the statistical characteristics of the True Random Number Generation (TRNG) were assessed using an autocorrelation study and the statistical test suites.
Random numbers are the lifeline of any cryptographic operation in modern computing. Quantum True Random Number Generator (QTRNG) can yield real random data to replace random-looking periodic sequences. Here, we present the fastest QTRNG on factual Quantum Computational Devices (QCD) locally on our device to save time. To construct a random number generator, the IBM Q Experience called Qiskit is used. The ease of our source Hadamard gate pooled with its verifiably sole randomness is imperative for attaining quantum random number generators at no cost. The inner matrix product of the square of the Hadamard gate is proved to be identified in the quantum lab. Applying the H gate twice on the quantum register cancelled out the H gate pair. It acted as a single Hadamard gate that was practically visualised using the IMB Q experience. The 24-qubit random number is experimented with and investigated in this work. The min-entropy of such generated random numbers is 0.0007244 with the worst-case entropy value of 0.999507. Besides, subsequent verification of genuine randomness was confirmed by steering restart experimentation, & the statistical properties of TRNG were evaluated over autocorrelation study, National Institute of Standards and Technology (NIST) SP 800-90B & 800-22 tests.
We propose to develop quantum true random number generation (QTRNG) in IBM's quantum laboratory by implementing the Random Quantum Fourier Transform (RQFT) technique utilizing quantum gates. This approach overcomes the imperfections caused by noise and decoherence in quantum circuits by utilizing the quantum Fourier transform (QFT) to replace the computational basis with a Fourier basis comprising a complex superposition of qubit states. For usage in symmetric and asymmetric cryptographic applications, the RQFT-based QTRNG produces unpredictable high quality random numbers. It satisfies all the preliminary tests, Autocorrelation test, and extensive National Institute of Standards and Technology (NIST) test suites, such as SP 800-90 and SP 800-22 are used to assess the generated sequences randomness in order to guarantee their robustness and reliability.
Quantum Random Number Generators (QRNGs) exploit intrinsic quantum mechanical randomness and are promising candidates for higher-quality randomness than classical RNGs. However, QRNGs on Noisy Intermediate-Scale Quantum (NISQ) platforms are constrained by decoherence, gate and measurement errors, and limited circuit depth, and the resulting impact on statistical randomness is not yet systematically characterized. This work introduces parameterized quantum random number generator (PQRNG) architectures built from parameterized quantum circuits (PQCs) to improve tunability and expressive power under realistic noise. We study three architectures, PQC-H-CH, H-PQC-CH, and H-CH-PQC, and evaluate their randomness under Transpiler optimization and circuit-level error mitigation as preprocessing steps, using NIST SP 800-22 for statistical validation. Utilizing default NIST settings, PQC-H-CH attains the largest number of fully passing preprocessing configurations (126 combinations passing all 15 tests), compared with 110 for H-PQC-CH and 69 for H-CH-PQC, indicating strong robustness across Transpiler optimization and mitigation settings, and we find that preprocessing choices influence randomness quality more strongly than adjusting NIST test parameters. Overall, these results demonstrate that the proposed approach provides a solid foundation for developing more reliable and practical QRNGs using PQCs in NISQ devices.