Quantum mechanics offers a fundamentally unpredictable entropy source due to the intrinsic probabilistic nature of quantum measurements, making it attractive for secure random number generation. This paper explores the practicality of generating random numbers from two quantum platforms: gate-based circuits on IBM Quantum and (Gaussian) boson sampling with Xanadu Borealis. We implement simple post-processing methods, including the classic Von Neumann extractor and two tailored variants designed to address the correlated structure of boson sampling outputs. We evaluate debiased output from real quantum hardware using the NIST SP800-22r1a test suite and measure the extraction efficiency of each debiasing method. Results show that, while unbiased bitstreams can be achieved on both platforms, throughput remains low and cost per random bit is high compared to specialized QRNG devices.
Quantum mechanics offers a fundamentally unpredictable entropy source due to the intrinsic probabilistic nature of quantum measurements, making it attractive for secure random number generation. This paper explores the practicality of generating random numbers from two quantum platforms: gate-based circuits on IBM Quantum and (Gaussian) boson sampling with Xanadu Borealis. We implement simple post-processing methods, including the classic Von Neumann extractor and two tailored variants designed to address the correlated structure of boson sampling outputs. We evaluate debiased output from real quantum hardware using the NIST SP800-22r1a test suite and measure the extraction efficiency of each debiasing method. Results show that, while unbiased bitstreams can be achieved on both platforms, throughput remains low and cost per random bit is high compared to specialized QRNG devices.
Abstract Designing novel circuits and models for generating truly random numbers is of great importance. Recent attention has been focused on the idea of using real quantum computers to generate random numbers. In order to generate purely random bits and numbers, one method relies on the natural superposition of quantum gates. Here, we design a Quantum Random Number Generator (QRNG) exploiting combinations of Hadamard and controlled-Hadamard gates in IBM Quantum Experience (QX). In order to verify that the generated bit sequences were truly random, the proposed circuit’s outputs were subjected to the National Institute of Standards and Technology (NIST) test suite. In comparison to previous works, we found that not only had the total number of passed tests increased, but the P-values had also improved in many cases. For example, in the simulation style of the IBM QX, the number of passed tests is 14 out of 15 standard NIST tests. For the experimental mood of the IBM 16Q Melbourne, the number of passed tests is six without using any extractions. Furthermore, one more test is passed by adding Barrier operations to the circuits, and the P-values have increased. As a result, we found that the proposed method had more passed tests than previous works. The average of P-values for the six passed tests in former research for the mood of using a combination of extractors is about 0.43 . However, the best result we can get with our circuits is around 0.60 (the average of P-values for 14 passed tests), which is an improvement of 0.17 compared to former works . Because of this, our circuits are getting ever closer to being a true QRNG.
The reliability and security of telecommunication systems depend on the quality of random number generators (RNGs). Quantum Random Number Generators (QRNGs) enhance randomness over classical methods, improving cryptographic strength. This paper evaluates three QRNG implementations using IBM quantum computers, analyzing their methodologies, performance, and limitations. We introduce a new QRNG algorithm on the IBM Quantum Experience (IBMQ) platform, using real quantum computers instead of simulators, and incorporating post-processing with XOR bit masking. Our algorithm achieves entropy 0.86458 and passes more NIST 800-90B and NIST 800–22 randomness tests, demonstrating its effectiveness and reliability. This research supports secure telecommunication technologies by providing a reliable source of true randomness.
Randomness is widely used in various fields, including encryption, statistical analysis, and numerical simulations. Randomness is also a fundamental resource in science and engineering. For such applications, we usually need to provide unbiased and independent random bits. This raises the issue of where to get these supposed random bits. Quantum random number generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. In practice, unfortunately, quantum randomness is inevitably mixed with classical randomness due to classical noise. Also, randomness is often correlated and biased. It is necessary to process the resulting raw bits sequence and convert them to good-quality output values that are as close to uniform distribution as possible. Random extractors are required for this. We analyze the randomness obtained by quantum random number generators as well as various examples of post-processing. We discuss the types of randomness extractors. Also, we use information-theoretically secure extractors and present improved novel randomness extraction.
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.
This chapter introduces a quantum phase-parameterized approach to true random number generation, utilizing a natural superposition of quantum gates. The method employs a scalable quantum circuit architecture, using the RY(π/2) gate followed by the S gate across multiple qubits to generate maximally unpredictable quantum states. The proposed circuit was implemented on the IBM Quantum platform, where it generates bitstrings from quantum measurements that form the basis of the QTRNG output. To validate the randomness and cryptographic strength, extensive statistical testing was performed. These include restart experiments to verify non-reproducibility, autocorrelation analysis for temporal independence, and evaluations using NIST SP 800-22, SP 800-90B, Dieharder, and ENT test suites. These tests evaluate frequency, runs, entropy, chi-square distribution, and serial correlation to ensure high entropy and strong randomness quality. Results confirm that the proposed QTRNG consistently passes all tests, generating unbiased, high-quality random numbers. The approach is suitable for applications in post-quantum cryptography, secure key generation, and communication protocols, establishing a robust framework for safe and scalable quantum-based randomness.
Random numbers are a crucial component of any encryption activity in modern cryptography. Quantum Random Number Generators (QRNGs) produce truly random output strings to replace pseudo-random ones. The principle of QRNG relies on measuring qubit states, which excel in quantum computing applications, particularly on IBM's quantum computing platform. To construct a random number generator, the authors utilized IBM Q Experience's Qiskit quantum development toolkit. We developed QRNG applications on IBM quantum computers (7-qubit, 16-qubit, and 127-qubit) and tested the program's functionality on these quantum computing platforms. The quality assessment of the random strings was conducted according to NIST and AIS-31 standards. For NIST standards, to achieve good quality, the output string must reach a minimum of 1,593,088 bits to pass 16 tests per SP800-22 standard. According to AIS-31 standards, to achieve good quality, the output string must reach a minimum of 8,000,000 bits to pass 8 tests of the standard