A comprehensive review of quantum random number generators: concepts, classification and the origin of randomness

Mannalatha, Vaisakh; Mishra, Sandeep; Pathak, Anirban

doi:10.1007/s11128-023-04175-y

Cited by 33 publications

(6 citation statements)

References 195 publications

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“…The majority of random number generators adhere to a fundamental principle that can be segmented into three key blocks: (1) Entropy Source; (2) Measurement; and (3) Postprocessing. According to publications [6] and [18], the operating process principle is understood as follows:…”

Section: The General Principle Of Quantum Random Number Generator (Qrng)mentioning

confidence: 99%

“… Lastly, the output of the measurement system may consist of raw random numbers, then undergo processing through a post-processing module to enhance their statistical and security properties. In the publication [6] several principles are proposed for creating diverse types of QRNGs based on distinct quantum random sources: encompass generating random numbers through the time arrival principle of emission states (utilizing qubit state measurements [19], [20]), employing photon detection or vacuum fluctuations ( [21], [22]), utilizing phase modulation principles in spontaneous emission [9], developments in non-optical QRNGs or QRNGs independent of specific devices [23].…”

Section: The General Principle Of Quantum Random Number Generator (Qrng)mentioning

confidence: 99%

“…Recently in the period from 2019-2024, with the development of electronic technology, the quantum random number generator (QRNG) has garnered significant attention and research interest from scientists and the scientific community [6]. The using of a Quantum random number generator (QRNG) aims to leverage the advantages of optical properties proposed by utilizing various random sources, including (1) Novel optical devices targeting single photon detection [7], [8]; (2) Devices measuring vacuum state fluctuations noise [9], [10]; (3) A laser phase-oscillator device [11] and amplify spontaneously emitted noise [12],... A QRNG generator typically comprises two primary components: (1) a random source and (2) a measurement unit.…”

Section: Introductionmentioning

confidence: 99%

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Implement quantum random number generation on the IBM quantum computer platform

Nhu Quynh,

Van Anh

2024

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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

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Section: The General Principle Of Quantum Random Number Generator (Qrng)mentioning

confidence: 99%

Section: The General Principle Of Quantum Random Number Generator (Qrng)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implement quantum random number generation on the IBM quantum computer platform

Nhu Quynh,

Van Anh

2024

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“…On the other hand, when random numbers are generated using quantum mechanical techniques, unpredictability is guaranteed by the inherent randomness of quantum mechanics [4,11,12]. A great variety of quantum random number generation schemes have been successfully realized experimentally using photonics, including branching paths [13][14][15], time-of-arrival [16][17][18][19][20][21][22][23][24], photon counting [25][26][27], vacuum fluctuations [28][29][30][31][32][33] and laser phase fluctuations [34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Quantum random number generation using an on-chip nanowire plasmonic waveguide

Strydom,

Soleymani,

Özdemir

et al. 2024

New J. Phys.

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Quantum random number generators employ the inherent randomness of quantum mechanics to generate truly unpredictable random numbers, which are essential in cryptographic applications. While a great variety of quantum random number generators have been realised using photonics, few exploit the high-field confinement offered by plasmonics, which enables device footprints an order of magnitude smaller in size. Here we integrate an on-chip nanowire plasmonic waveguide into an optical time-of-arrival based quantum random number generation setup. Despite loss, we achieve a random number generation rate of $14.4\,\text{Mbits/s}$ using low light intensity, with the generated bits passing industry standard tests without post-processing. By increasing the light intensity, we were then able to increase the generation rate to $41.4\,\text{Mbits/s}$, with the resulting bits only requiring a shuffle to pass all tests. This is an order of magnitude increase in the generation rate and decrease in the device size compared to previous work. Our experiment demonstrates the successful integration of an on-chip nanoscale plasmonic component into a quantum random number generation setup. This may lead to new opportunities in compact and scalable quantum random number generation.

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“…However, many of the numbers currently used under the premise of being random are not genuinely so, as they originate from deterministic algorithms or phenomena. These numbers are termed pseudo-random, for although they may satisfy tests for randomness, they are based on seeds that are not intrinsically random to generate longer sequences [11]. To address this issue, one turns to quantum physics and quantum principles, such as superposition, which are inherently unpredictable and cannot be replicated.…”

Section: Introductionmentioning

confidence: 99%

Quantum Random Number Generation using Quandela Photonic Quantum Computer

de Souza,

Agostini,

Tarelho

2024

Preprint

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Quantum mechanics, characterized by its intrinsically probabilistic nature, offers a promising avenue for random number generation, which is essential for applications such as cryptography and computational simulations. With the recent advancements in quantum computing and simulation, numerous studies have emerged utilizing these methods for the generation of random numbers. This research delves into the exploration of random number generation utilizing the Ascella photonic quantum computer developed by Quandela, renowned for its implementation of single-photon-based qubits. Leveraging both the Ascella photonic simulator (SIM Ascella) and the quantum processing unit (QPU Ascella) within the Perceval framework, this investigation examines the capability to generate random sequences through the superposition of quantum states, generated using photons and beam splitters. The analysis includes a performance comparison between simulations and experimental tests with the quantum computer, subjecting the outcomes to the NIST SP 800-22 randomness tests. While initial simulations suggested a high degree of randomness, practical implementation revealed certain disparities attributed to factors such as decoherence, imperfections in beam splitters and single-photon sources, as well as quantum noise. This study contributes to the understanding of random number generation on quantum platforms, identifying challenges and limitations while providing strategies for future enhancements in this quantum technology.

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A comprehensive review of quantum random number generators: concepts, classification and the origin of randomness

Cited by 33 publications

References 195 publications

Implement quantum random number generation on the IBM quantum computer platform

Implement quantum random number generation on the IBM quantum computer platform

Quantum random number generation using an on-chip nanowire plasmonic waveguide

Quantum Random Number Generation using Quandela Photonic Quantum Computer

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