A brief overview of programmed instructions for quantum software education

• URL: http://arxiv.org/abs/2312.10020v1
• Date: Wed, 13 Dec 2023 08:47:08 GMT
• Title: A brief overview of programmed instructions for quantum software education
• Authors: Richard A. Wolf, Sho Araiba
• Abstract summary: The article presents the programmed instructions method and recent successes in STEM fields before describing its operating mode. Elements tackled include the core components of programmed instructions, its roots and early use as well as adaptation to complex STEM material. The aim of this work is to provide high-level guidelines for incorporating programmed instructions in quantum education with the goal of disseminating quantum skills and notions more efficiently to a wider audience.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper we provide an overview of the programmed instructions approach for the purpose of quantum software education. The article presents the programmed instructions method and recent successes in STEM fields before describing its operating mode. Elements tackled include the core components of programmed instructions, its behavioural roots and early use as well as adaptation to complex STEM material. In addition, we offer recommendations for its use in the specific context of quantum software education and provide one example of PI-based instruction for the notion of entanglement. The aim of this work is to provide high-level guidelines for incorporating programmed instructions in quantum education with the goal of disseminating quantum skills and notions more efficiently to a wider audience.

Related papers

• Refinement calculus of quantum programs with projective assertions [5.151896714190243]
  This paper introduces a refinement calculus tailored for quantum programs. We first study the partial correctness of nondeterministic programs within a quantum. We also present their semantics in transforming a postcondition to the weakest liberal postconditions.
  arXiv  Detail & Related papers  (2023-11-23T22:12:57Z)
• Symbolic quantum programming for supporting applications of quantum computing technologies [0.0]
  The main focus of this paper is on quantum computing technologies, as they can in the most direct way benefit from developing tools. We deliver a short survey of the most popular approaches in the field of quantum software development and we aim at pointing their strengths and weaknesses. Next, we describe a software architecture and its preliminary implementation supporting the development of quantum programs using symbolic approach.
  arXiv  Detail & Related papers  (2023-02-18T18:30:00Z)
• Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs [58.94569213396991]
  We propose a hierarchical programmatic reinforcement learning framework to produce program policies. By learning to compose programs, our proposed framework can produce program policies that describe out-of-distributionally complex behaviors. The experimental results in the Karel domain show that our proposed framework outperforms baselines.
  arXiv  Detail & Related papers  (2023-01-30T14:50:46Z)
• Developing Programming Assignments for Teaching Quantum Computing and Quantum Programming [0.8158530638728501]
  This report describes a variety of programming assignments that can be used to teach quantum computing in a practical manner. These assignments let the learners get hands-on experience with all stages of quantum software development process.
  arXiv  Detail & Related papers  (2022-11-27T20:32:53Z)
• Modern applications of machine learning in quantum sciences [51.09906911582811]
  We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms. We discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
  arXiv  Detail & Related papers  (2022-04-08T17:48:59Z)
• ProTo: Program-Guided Transformer for Program-Guided Tasks [59.34258016795216]
  We formulate program-guided tasks which require learning to execute a given program on the observed task specification. We propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program. ProTo executes a program in a learned latent space and enjoys stronger representation ability than previous neural-symbolic approaches.
  arXiv  Detail & Related papers  (2021-10-02T13:46:32Z)
• How could Neural Networks understand Programs? [67.4217527949013]
  It is difficult to build a model to better understand programs, by either directly applying off-the-shelf NLP pre-training techniques to the source code, or adding features to the model by theshelf. We propose a novel program semantics learning paradigm, that the model should learn from information composed of (1) the representations which align well with the fundamental operations in operational semantics, and (2) the information of environment transition.
  arXiv  Detail & Related papers  (2021-05-10T12:21:42Z)
• Teaching Quantum Computing through a Practical Software-driven Approach: Experience Report [0.913755431537592]
  There is rapidly growing demand for a quantum workforce educated in the basics of quantum computing. There are few offerings for non-specialists and little information on best practices for training computer science and engineering students. We describe our experience teaching an undergraduate course on quantum computing using a practical, software-driven approach.
  arXiv  Detail & Related papers  (2020-10-12T06:16:54Z)
• Extending XACC for Quantum Optimal Control [70.19683407682642]
  Quantum computing vendors are beginning to open up application programming for direct pulse-level quantum control. We present an extension to the XACC system-level quantum-classical software framework. This extension enables the translation of digital quantum circuit representations to equivalent pulse sequences.
  arXiv  Detail & Related papers  (2020-06-04T13:13:55Z)
• The ILASP system for Inductive Learning of Answer Set Programs [79.41112438865386]
  Our system learns Answer Set Programs, including normal rules, choice rules and hard and weak constraints. We first give a general overview of ILASP's learning framework and its capabilities. This is followed by a comprehensive summary of the evolution of the ILASP system.
  arXiv  Detail & Related papers  (2020-05-02T19:04:12Z)

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.