Quantum Computing: an undergraduate approach using Qiskit
- URL: http://arxiv.org/abs/2101.11388v1
- Date: Tue, 26 Jan 2021 18:19:23 GMT
- Title: Quantum Computing: an undergraduate approach using Qiskit
- Authors: Gleydson Fernandes de Jesus, Maria Helo\'isa Fraga da Silva, Teonas
Gon\c{c}alves Dourado Netto, Lucas Queiroz Galv\~ao, Frankle Gabriel de
Oliveira Souza and Clebson Cruz
- Abstract summary: We present the Quantum Information Software Developer Kit - Qiskit, for teaching quantum computing to undergraduate students.
We focus on presenting the construction of the programs on any common laptop or desktop computer and their execution on real quantum processors.
The codes are made available throughout the text so that readers, even with little experience in scientific computing, can reproduce them.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we present the Quantum Information Software Developer Kit -
Qiskit, for teaching quantum computing to undergraduate students, with basic
knowledge of quantum mechanics postulates. We focus on presenting the
construction of the programs on any common laptop or desktop computer and their
execution on real quantum processors through the remote access to the quantum
hardware available on the IBM Quantum Experience platform. The codes are made
available throughout the text so that readers, even with little experience in
scientific computing, can reproduce them and adopt the methods discussed in
this paper to address their own quantum computing projects. The results
presented are in agreement with theoretical predictions and show the
effectiveness of the Qiskit package as a robust classroom working tool for the
introduction of applied concepts of quantum computing and quantum information
theory.
Related papers
- The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - Quantum computing: principles and applications [3.717431207294639]
We introduce the basic principles of quantum computing and the multilayer architecture for a quantum computer.
Based on a mature experimental platform, the Nuclear Magnetic Resonance (NMR) platform, we introduce the basic steps to experimentally implement quantum computing.
arXiv Detail & Related papers (2023-10-13T20:12:28Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Quantum Machine Learning Implementations: Proposals and Experiments [0.0]
The article reviews specific high-impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors.
The field of quantum machine learning could be among the first quantum technologies producing results that are beneficial for industry and, in turn, to society.
arXiv Detail & Related papers (2023-03-11T01:02:16Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
Quantum generative learning models (QGLMs) may surpass their classical counterparts.
We review the current progress of QGLMs from the perspective of machine learning.
We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
arXiv Detail & Related papers (2022-06-07T07:32:57Z) - Evolution of Quantum Computing: A Systematic Survey on the Use of
Quantum Computing Tools [5.557009030881896]
We conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing.
We discuss the current essence, identify open challenges and provide future research direction.
We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
arXiv Detail & Related papers (2022-04-04T21:21:12Z) - An Introduction to Quantum Computing for Statisticians [2.3757641219977392]
Quantum computing has the potential to revolutionise and change the way we live and understand the world.
This review aims to provide an accessible introduction to quantum computing with a focus on applications in statistics and data analysis.
arXiv Detail & Related papers (2021-12-13T12:08:28Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Quantum Computation [0.0]
We will discuss and summarized the core principles and practical application areas of quantum computation.
The mapping of computation onto the behavior of physical systems is a historical challenge.
We will evaluate the essential technology required for quantum computers to be able to function correctly.
arXiv Detail & Related papers (2020-06-04T11:57:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.