Teaching quantum information science to high-school and early undergraduate students

• URL: http://arxiv.org/abs/2005.07874v3
• Date: Sat, 8 Aug 2020 18:50:53 GMT
• Title: Teaching quantum information science to high-school and early undergraduate students
• Authors: Sophia E. Economou, Terry Rudolph, Edwin Barnes
• Abstract summary: This program allows students to perform meaningful hands-on calculations with quantum circuits and algorithms. A combination of pen-and-paper exercises and IBM Q simulations helps students understand the structure of quantum gates and circuits.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a simple, accessible, yet rigorous outreach/educational program focused on quantum information science and technology for high-school and early undergraduate students. This program allows students to perform meaningful hands-on calculations with quantum circuits and algorithms, without requiring knowledge of advanced mathematics. A combination of pen-and-paper exercises and IBM Q simulations helps students understand the structure of quantum gates and circuits, as well as the principles of superposition, entanglement, and measurement in quantum mechanics.

Related papers

• 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)
• Semidefinite Programming in Quantum Information Science [0.0]
  Semidefinite programs (SDPs) are optimisation problems that find application in numerous areas of physics, engineering and mathematics. SDPs are particularly suited to problems in quantum physics and quantum information science. Specific applications include quantum state, measurement, and channel estimation and discrimination.
  arXiv  Detail & Related papers  (2023-06-20T16:04:38Z)
• 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)
• Investigating students' strengths and difficulties in quantum computing [0.0]
  An ongoing race to develop practical quantum computers and increase the quantum workforce. This needs to be accompanied by the development of quantum computing programs, courses, and curricula. We introduced an introductory course in quantum computing to undergraduate students and investigated the strengths and difficulties of these students.
  arXiv  Detail & Related papers  (2022-12-05T23:46:20Z)
• Hello Quantum World! A rigorous but accessible first-year university course in quantum information science [0.0]
  Hello Quantum World! introduces a broad range of fundamental quantum information and computation concepts. Some of the topics covered include superposition, entanglement, quantum gates, teleportation, quantum algorithms, and quantum error correction.
  arXiv  Detail & Related papers  (2022-09-25T18:59:47Z)
• 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)
• From Quantum Graph Computing to Quantum Graph Learning: A Survey [86.8206129053725]
  We first elaborate the correlations between quantum mechanics and graph theory to show that quantum computers are able to generate useful solutions. For its practicability and wide-applicability, we give a brief review of typical graph learning techniques. We give a snapshot of quantum graph learning where expectations serve as a catalyst for subsequent research.
  arXiv  Detail & Related papers  (2022-02-19T02:56:47Z)
• 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 Computing: an undergraduate approach using Qiskit [0.0]
  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.
  arXiv  Detail & Related papers  (2021-01-26T18:19:23Z)
• Quantum walk processes in quantum devices [55.41644538483948]
  We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
  arXiv  Detail & Related papers  (2020-12-28T18:04:16Z)

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.