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arXiv:1906.04410 · Mathematics for industry

Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer

Kentaro Tamura, Yutaka Shikano

Published Oct 22, 2020• 30 citations
arXivHTMLPDF

Abstract

Abstract A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to its users. In this respect, a cloud quantum computer is a black box. In both devices, its users decide the device condition from the output. A framework to achieve this exists in the field of random number generation in the form of statistical tests for random number generators. In the present study, we generated random numbers on a 20-qubit cloud quantum computer and evaluated the condition and stability of its qubits using statistical tests for random number generators. As a result, we observed that some qubits were more biased than others. Statistical tests for random number generators may provide a simple indicator of qubit condition and stability, enabling users to decide for themselves which qubits inside a cloud quantum computer to use.

DOI
10.1007/978-981-15-5191-8_6

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Abstract A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to its users. In this respect, a cloud quantum computer is a black box. In both devices, its users decide the device condition from the output. A framework to achieve this exists in the field of random number generation in the form of statistical tests for random number generators. In the present study, we generated random numbers on a 20-qubit cloud quantum computer and evaluated the condition and stability of its qubits using statistical tests for random number generators. As a result, we observed that some qubits were more biased than others. Statistical tests for random number generators may provide a simple indicator of qubit condition and stability, enabling users to decide for themselves which qubits inside a cloud quantum computer to use.
Creating a true random number generator is a difficult problem that may be solved by leveraging quantum computing. A simple quantum random number generation program involves applying the Hadamard gate to all qubits then measuring each qubit for random data, repeating the program to obtain the desired bitstring length. Implementing this algorithm on IBM’s Manila and Rigetti’s Aspen-9 quantum computers, we obtained a sample of 1,000,000 potentially random bits. In this thesis, we evaluate the quality of random bits generated by using quantum computers as a hardware random number generator with statistical testing and present the implications of those results.
All computing devices, including quantum computers, must exhibit that for a given input, an output is produced in accordance with the program. The outputs generated by quantum computers that fulfill these requirements are not temporally correlated, however. In a quantum-computing device comprising solid-state qubits such as superconducting qubits, any operation to rest the qubits to their initial state faces a practical problem. We applied a statistical analysis to a collection of random numbers output from a 20-qubit superconducting-qubit cloud quantum computer using the simplest random number generation scheme. The analysis indicates temporal correlation in the output of some sequences obtained from the 20 qubits. This temporal correlation is not related to the relaxation time of each qubit. We conclude that the correlation could be a result of a systematic error.
Objectives . The absence of error-resistant quantum computers, coupled with the challenges associated with providing unrestricted and fully operational physical access to cloud quantum computing systems, prompts a critical examination of the necessity to develop universal and independent methods for evaluating and verifying cloud quantum computers. A promising approach involves evaluating the capabilities of a quantum computer in relation to its effectiveness in addressing specific challenges encountered in the assessment of information security systems. A potential test for ascertaining the performance and computational quality of a quantum computing device (QCD) is based on a model designed to generate a random binary sequence. By analyzing this sequence, insights can be obtained into the accuracy and reliability of the quantum register under study. The paper presents a software program developed for simulating the operation of a quantum random number generator. Methods . The software implementation for interacting with cloud quantum computers was performed using the Qiskit open-source software kit. The graphical user interface of the software package was developed using a Qt5 crossplatform set of tools and widgets for creating applications. The analysis of the generated binary sequence was performed using a set of statistical tests NIST STS 2 . Results . The developed software package provides users with a graphical interface for conducting an analysis of a cloud QCD to identify the optimal and most error-resistant set of qubits. The findings from experiments conducted on three cloud quantum computing devices are reported. Conclusions . The proposed approach, which is constrained by limitations of computing power and duration of access to cloud-based QCD, imposes minimal demands on the productive capabilities of the quantum system. It offers clear and unequivocally interpretable insights into the technical characteristics of a cloud quantum computer, while also being reproducible, easily scalable, and universally applicable.
Abstract Random numbers form an intrinsic part of modern day computing with applications in a wide variety of fields, and quantum systems due to their intrinsic randomness form a suitable candidate for generation of true random numbers that can also be certified. In this work, we have demonstrated the use of cloud based quantum computers to develop a partially loophole free device-independent quantum random number generator (QRNG). The generated random numbers have been tested for their source of origin through experiments based on the testing of Clauser, Horne, Shimony, and Holt (CHSH) inequality through available IBM quantum computers. The performance of each quantum computer against the CHSH test has been plotted and characterized. Further, efforts have been made to close as many loopholes as possible to produce device-independent quantum random number generators. This study will help provide new directions for the development of self-testing and semi-self-testing random number generators using quantum computers.
Abstract Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum devices. A common property of quantum computers is that they can exhibit instances of true randomness as opposed to pseudo-randomness obtained from classical systems. Investigating the effects of such true quantum randomness in the context of machine learning is appealing, and recent results vaguely suggest that benefits can indeed be achieved from the use of quantum random numbers. To shed some more light on this topic, we empirically study the effects of hardware-biased quantum random numbers on the initialization of artificial neural network weights in numerical experiments. We find no statistically significant difference in comparison with unbiased quantum random numbers as well as biased and unbiased random numbers from a classical pseudo-random number generator. The quantum random numbers for our experiments are obtained from real quantum hardware.
A cloud quantum computer is a quantum computer that can be accessed in a cloud environment through a network. Today, there are numbers of cloud quantum computing services that can be accessed by users. They are used to solve complex problems that require powerful computing. Different cloud quantum computing services deliver different architecture and performances. In our study, we conducted a research on some services to test and evaluate the performances of different cloud quantum computing services and make a comparison out of it. The test will be conducted using two different methods such as visual programming and qiskit. From the result, we can see that the amount of qubit per backend and shots per run pretty much affect the execution time of a cloud quantum computing. This test will give the users some insight and enables them to decide which cloud quantum computing services deliver better performance or faster execution time based on the specification each cloud quantum computer offers.