Quantum true random number generation on IBM’s cloud platform

Kumar, Vaishnavi; Rayappan, John Bosco Balaguru; Amirtharajan, Rengarajan; Praveenkumar, Padmapriya

doi:10.1016/j.jksuci.2022.01.015

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…The Hadamard gate demonstrates its ability to generate a 50% chance of measuring the quantum state |0⟩ and an equally 50% chance of measuring |1⟩. This property serves as the foundation for the proposal put forth in a study by Kumar et al 15 . The document establishes that leveraging multiple Hadamard gates can lead to the creation of random sequences of numbers.…”

Section: Uniform Quantum Probability Distribution With Hadamard Gatementioning

confidence: 87%

Exploring Quantum Systems for Pseudo-Random Number Generation

Cruz,

Faina,

Pereira

2023

Preprint

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Quantum realizations have opened a new ways for computer science. The new characteristics and phenomenons allow us to create new algorithms for general proposes. An interesting topic is random number generation. There are two perspectives for this proposes. True random numbers generator (TRN) and pseudo random numbers generator (PRNG). True Random Number Generators has four significant characteristics: randomness, long Period, insensitivity to seeds, repeatability. Additionally, Pseudo Random Number Generators is a fascinating topic that leverages the principles of computer science to craft deterministic algorithms, enabling the creation of a sequence of pseudo-random numbers. A crucial element in this process is the 'seed', which serves as the initial value to generate the entire sequence. This document proposes a algorithm based on a quantum system and the quasi-probability of its possible states to generate a long set of numbers which has pseudo random properties. The quantum system is compound by n qubits, its initial conditions and a small set of rotation gates with a fixed angle. The tests were realized using the battery test called NIST SP800-22, and alpha=0.01 as a threshold to ensures 99\% of precision. The results demonstrate that it is possible generate a long set of pseudo numbers with random characteristics using a quantum simple operation. However, the experiments reveal that a real world implementation require a quantum system with at least 10-9 precision order.

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Section: Uniform Quantum Probability Distribution With Hadamard Gatementioning

confidence: 87%

Exploring Quantum Systems for Pseudo-Random Number Generation

Cruz,

Faina,

Pereira

2023

Preprint

View full text Add to dashboard Cite

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“…The generation of random numbers using quantum computers available in the cloud is a rapidly evolving field, with a growing number of publications over the last 5 years [13][14][15][16][17][18][32][33][34][35]. These publications are based on quantum superposition, which is a property of quantum mechanics that distinguishes it from classical physics.…”

Section: Methodsmentioning

confidence: 99%

“…This represents the simplest example of measuring qubits in superposition to generate random numbers; however, the results obtained showed that the samples were biased and correlated, necessitating post-processing to pass statistical tests [14]. Subsequent papers have been published using multiple H gates [15] and other combinations of quantum logic gates [16][17][18].…”

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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“…Additionally, the authors observed differences in execution time when running the QRNG program on a virtual machine (a local computer with Qiskit installed, an open-source software development kit that runs notably faster than sending jobs to IBM quantum computers for execution). This discrepancy stems from the time required to transmit and receive results from the quantum computer back to the user's computer [29], [30].…”

Section: C) Modulo Qrng (127-qubit) Setup On the Ibm Virtual Computer...mentioning

confidence: 99%

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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Quantum true random number generation on IBM’s cloud platform

Cited by 11 publications

References 29 publications

Exploring Quantum Systems for Pseudo-Random Number Generation

Exploring Quantum Systems for Pseudo-Random Number Generation

Quantum Random Number Generation using Quandela Photonic Quantum Computer

Implement quantum random number generation on the IBM quantum computer platform

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