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.
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.
Quantum computing has emerged as a revolutionary approach to solving complex problems that demand powerful computational capabilities. With various cloud quantum computing services available, users are presented with different architectures and performances. In this study, we conduct research on select cloud quantum computing services to evaluate their performances and compare them. Using two different methods, visual programming and Qiskit, we test these services and analyze the impact of factors such as the number of qubits per backend and shots per run on execution time. Our findings provide users with insights to decide which cloud quantum computing services offer better performance or faster execution times based on their specific requirements.
Quantum Computing (QC) has evolved from a few custom quantum computers, which were only accessible to their creators, to an array of commercial quantum computers that can be accessed on the cloud by anyone. Accessing these cloud quantum computers requires a complex chain of tools that facilitate connecting, programming, simulating algorithms, estimating resources, submitting quantum computing jobs, retrieving results, and more. Some steps in the chain are hardware dependent and subject to change as both hardware and software tools, such as available gate sets and optimizing compilers, evolve. Understanding the trade-offs inherent in this process is essential for evaluating the power and utility of quantum computers. ARLIS has been systematically investigating these environments to understand these complexities. The work presented here is a detailed summary of three months of using such quantum programming environments. We show metadata obtained from these environments, including the connection metrics to the different services, the execution of algorithms, the testing of the effects of varying the number of qubits, comparisons to simulations, execution times, and cost. Our objective is to provide concrete data and insights for those who are exploring the potential of quantum computing. It is not our objective to present any new algorithms or optimize performance on any particular machine or cloud platform; rather, this work is focused on providing a consistent view of a single algorithm executed using out-of-the-box settings and tools across machines, cloud platforms, and time. We present insights only available from these carefully curated data.
Generation of random numbers is a central problem for many applications in the field of information processing, including, e.g., cryptography, in classical and quantum regime, but also mathematical modeling, Monte Carlo methods, gambling and many others. Both, the quality of the randomness and efficiency of the random numbers generation process are crucial for the most of these applications. Software produced pseudorandom bit sequences, though sufficiently quick, do not fulfill required randomness quality demands. Hence, the physical hardware methods are intensively developed to generate truly random number sequences for information processing and electronic security application. In the present paper we discuss the idea of the quantum random number generators. We also present a variety of tests utilized to assess the quality of randomness of generated bit sequences. In the experimental part we apply such tests to assess and compare two quantum random number generators, PQ4000KSI (of company ComScire US) and JUR01 (constructed in Wroclaw University of Science and Technology upon the project of The National Center for Research and Development) as well as a pseudorandom generator from the Mathematica Wolfram package. Finally, we present our new prototype of fully operative miniaturized quantum random generator JUR02 producing a random bit sequence with velocity of 1 Mb/s, which successfully passed standard tests of randomness quality (like NIST and Dieharder tests). We also shortly discuss our former concept of an entanglement-based quantum random number generator protocol with unconditionally secure public randomness verification.
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.
A quantum processor, like any computing device, requires the development of both hardware and the necessary set of software solutions, starting with quantum algorithms and ending with means of accessing quantum devices. As part of the roadmap for the development of the high-tech field of quantum computing in the period from 2020 to 2024, a set of software solutions for quantum computing devices was developed. This software package includes a set of quantum algorithms for solving prototypes of applied tasks, monitoring and benchmarking tools for quantum processors, error suppression and correction methods, tools for compiling and optimizing quantum circuits, as well as interfaces for remote cloud access. This review presents the key results achieved, among which it is necessary to mention the execution of quantum algorithms using a cloud-based quantum computing platform.
Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of Python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the QC software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of key features including a flexible optimization toolkit, engineering-inspired filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.