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EVALUATION OF A PHYSICAL QUANTUM COMPUTER

John A. Heropoulos

Published Jun 1, 2020• 0 citations
OpenAlex

Abstract

In the past few years, IBM has expanded their Quantum Experience to include a high-level Python module called Qiskit as well as a 15-qubit machine (ibmq_16_melbourne). In order to evaluate IBM’s progress, we create an overview of the processes involved in developing and running an algorithm on IBM’s quantum machines. This includes exploring the new resources that IBM has made available recently including the Qiskit Python module. Then, we test textbook algorithms such as the Deutsch-Jozsa algorithm, Grover’s search algorithm, and Quantum Fourier Transform on ibmq_16_melbourne and compare the results with the output of a simulator. Constraints imposed by IBM’s Quantum Experience and significant differences between algorithm performance on IBM's quantum hardware and the simulator will be used to assess IBM’s quantum computing capabilities.

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In the past few years, IBM has expanded their Quantum Experience to include a high-level Python module called Qiskit as well as a 15-qubit machine (ibmq_16_melbourne). In order to evaluate IBM’s progress, we create an overview of the processes involved in developing and running an algorithm on IBM’s quantum machines. This includes exploring the new resources that IBM has made available recently including the Qiskit Python module. Then, we test textbook algorithms such as the Deutsch-Jozsa algorithm, Grover’s search algorithm, and Quantum Fourier Transform on ibmq_16_melbourne and compare the results with the output of a simulator. Constraints imposed by IBM’s Quantum Experience and significant differences between algorithm performance on IBM's quantum hardware and the simulator will be used to assess IBM’s quantum computing capabilities.
One of the greatest technological advancements in the last century lies in digital computer science. The idea of storing information and performing complicated calculations with the help of bits, i.e., 0 and 1. But due to a sudden surge in data, the classical computer system has been becoming weak in data processing. Quantum computers offer promising substantial speedup over classical computers for many applications. Quantum chip fabrication has made remarkable gains in recent years, with the number of qubits and fidelity growing. In general computing, a binary digit is the smallest unit of information or a bit. “In quantum computing, the term Qubit (Quantum Bit) serves the exact function of the term bit.” IBM Research released the IBMQ Experience in 2018, the first quantum computer that anyone can use and make accessible to a huge audience of different countries through cloud access. IBM also introduced the tool QISKIT (Quantum information software kit), which enables teachers, researchers, and developers to write coding and run their coding on quantum machines. It also includes different packages of quantum computing. In this paper, the author has discussed different steps to install qiskit. Mainly this paper focused on the “programming and application side of quantum computing.” Qiskit tools used in the python programming language. The “quantum circuits” are fabricated with the use of quantum gates and favorable algorithms with less execution time
In May 2016 IBM released access to its 5-qubit quantum computer to the scientific community, its “IBM Quantum Experience” since acquiring over 60,000 users from students, educators and researchers around the globe. In the time since the “IBM Quantum Experience” became available, a flurry of research results on 5-qubit systems has been published derived from the platform hardware. Quintuple is an open-source object-oriented Python module implementing the ideal simulation of “IBM’s Quantum Experience” hardware. Quintuple quantum algorithms can be programmed and run via a custom language fully compatible with the “IBM’s Quantum Experience” or in pure Python. Over 40 example programs are provided with expected results, including Grover’s Algorithm and the Deutsch-Jozsa algorithm. Quintuple’s implementation is aimed at students and educators wishing to incorporate quantum computing into the classroom and enables students to follow a quantum computing calculation step-by-step and to verify hand calculations. For these students and educators, Quintuple contributes to the study of 5-qubit systems and the development and debugging of quantum algorithms for deployment on the “IBM Quantum Experience” hardware.
In May 2016 IBM released access to its 5-qubit quantum computer to the scientific community, its "IBM Quantum Experience" since acquiring over 25,000 users from students, educators and researchers around the globe. In the short time since the "IBM Quantum Experience" became available, a flurry of research results on 5-qubit systems have been published derived from the platform hardware. Quintuple is an open-source object-oriented Python module implementing the simulation of the "IBM Quantum Experience" hardware. Quintuple quantum algorithms can be programmed and run via a custom language fully compatible with the "IBM Quantum Experience" or in pure Python. Over 40 example programs are provided with expected results, including Grover's Algorithm and the Deutsch-Jozsa algorithm. Quintuple contributes to the study of 5-qubit systems and the development and debugging of quantum algorithms for deployment on the "IBM Quantum Experience" hardware.
Through the application of concepts derived from quantum mechanics, quantum computers assert that they are capable of doing certain tasks with more efficiency than classical computers. For the purpose of accelerating the process of solving unstructured search problems, a well-known quantum technique known as Grover’s algorithm, is utilized. As part of this investigation, the IBM Q 5 Tenerife is compared to the quantum hardware that is already available from IBM, and the feasibility of adopting Grover’s approach is evaluated. An analysis of the algorithm’s component pieces, such as the diffusion operator and the oracle, is included in this presentation. Additionally, simulation and experimental data are also included. The findings that we obtained demonstrate that the accuracy of the 3-qubit Grover’s approach is improved when it is executed. In contrast, the 4-qubit Grover’s technique demonstrates a rise in accuracy that is not as noticeable as the other algorithm. The execution time of the 3-qubit Grover’s approach is successfully reduced from 84.33 second to just 5 second by both of the methods, which is something that should be included in the discussion.
With the growth of quantum computing hardware technology, more developers are becoming involved in the design and development of quantum algorithms, leading to the creation of practical applications that align with real-world needs through the combination of quantum hardware and software. Among quantum algorithms, the most well-known include Grover’s algorithm and Shor’s algorithm. Grover’s algorithm can quickly provide search results, making it highly valuable for search-related applications. In light of this, this study primarily examines the theoretical usability of Grover’s algorithm and explores its limitations. Additionally, on the practical side, this research implements Grover’s algorithm using IBM Quantum Computing and the IBM Qiskit package. By comparing simulation results with those obtained from execution on real quantum computers, the study discusses the current limitations of quantum computing hardware technology.