Verifying the Reliability of Quantum Random Number Generator: A Comprehensive Testing Approach

Biswas, Rounak; Roy Talukdar, Dhruv; Roy, Utpal

doi:10.1007/s42979-023-02323-w

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…According to the publication [18], [29], Vaishnavi Kumar and colleagues employed an input of 24 qubits and 65536 shots to generate random numbers, producing 24 × 65536 bits in a single execution. In contrast, the author's group conducted experiments using input qubit numbers of 7, 16, or 127 qubits in their QRNG modulo.…”

Section: The Achieved Results Can Be Evaluated Using the Nist And Ais...mentioning

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 Achieved Results Can Be Evaluated Using the Nist And Ais...mentioning

confidence: 99%

“…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%

Implement quantum random number generation on the IBM quantum computer platform

Nhu Quynh,

Van Anh

2024

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“…Quantum superposition has profound implications for both quantum computing and the generation of random numbers. Quantum processes, such as quantum tunneling or photon splitting, generate random outcomes that, due to the superposition of quantum states, are intrinsically unpredictable, and these outcomes can thus be harnessed to obtain truly random numbers [12].…”

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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