Quantum Composer: A programmable quantum visualization and simulation tool for education and research

• URL: http://arxiv.org/abs/2006.07263v1
• Date: Fri, 12 Jun 2020 15:19:02 GMT
• Title: Quantum Composer: A programmable quantum visualization and simulation tool for education and research
• Authors: Shaeema Zaman Ahmed, Jesper Hasseriis Mohr Jensen, Carrie Ann Weidner, Jens Jakob S{\o}rensen, Marcel Mudrich and Jacob Friis Sherson
• Abstract summary: Quantum Composer allows the user to build, expand, or explore quantum mechanical simulations by interacting with graphically connectable nodes. We illustrate its open-ended applicability in both introductory and advanced quantum mechanics courses, student projects, and for visual exploration within research environments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Making quantum mechanical equations and concepts come to life through interactive simulation and visualization are commonplace for augmenting learning and teaching. However, graphical visualizations nearly always exhibit a set of hard-coded functionalities while corresponding text-based codes offer a higher degree of flexibility at the expense of steep learning curves or time investments. We introduce Quantum Composer, which allows the user to build, expand, or explore quantum mechanical simulations by interacting with graphically connectable nodes, each corresponding to a physical concept, mathematical operation, visualization, etc. Abstracting away numerical and programming details while at the same time retaining accessibility, emphasis on understanding, and rapid feedback mechanisms, we illustrate through a series of examples its open-ended applicability in both introductory and advanced quantum mechanics courses, student projects, and for visual exploration within research environments.

Related papers

• Quantum cryptography visualized: assessing visual attention on multiple representations with eye tracking in an AR-enhanced quantum cryptography student experiment [0.4004884551042419]
  We present an analysis of the representations in our AR-enhanced quantum cryptography student experiment. We also discuss learner visual attention with respect to the provided multiple representations based on the eye gaze data we have obtained.
  arXiv  Detail & Related papers  (2024-10-29T12:01:00Z)
• Quantum Multimodal Contrastive Learning Framework [0.0]
  We propose a novel framework for multimodal contrastive learning utilizing a quantum encoder to integrate EEG (electroencephalogram) and image data. We demonstrate that the quantum encoder effectively captures intricate patterns within EEG signals and image features, facilitating improved contrastive learning across modalities.
  arXiv  Detail & Related papers  (2024-08-25T19:08:43Z)
• QuReed [1.3654846342364306]
  QuReed is an open-source quantum simulation framework designed to bridge gaps between quantum theory, experimental community and engineering. By facilitating cross-talk between theory and experiments, QuReed aims to accelerate progress in the field and unlock the transformative power of quantum mechanics in the communications industry.
  arXiv  Detail & Related papers  (2024-06-11T18:11:46Z)
• 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)
• Discovering Quantum Circuit Components with Program Synthesis [6.390357081534995]
  We show how a computer can incrementally learn concepts relevant for quantum circuit synthesis with experience. We show how, starting from a set of elementary gates, we can automatically discover a library of new useful composite gates.
  arXiv  Detail & Related papers  (2023-05-02T18:17:07Z)
• Quantum Neural Network Classifiers: A Tutorial [1.4567067583556714]
  We focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks. benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language.
  arXiv  Detail & Related papers  (2022-06-06T18:00:01Z)
• 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)
• 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)
• Standard Model Physics and the Digital Quantum Revolution: Thoughts about the Interface [68.8204255655161]
  Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress. From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
  arXiv  Detail & Related papers  (2021-07-10T06:12:06Z)
• A backend-agnostic, quantum-classical framework for simulations of chemistry in C++ [62.997667081978825]
  We present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software. A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented.
  arXiv  Detail & Related papers  (2021-05-04T16:53:51Z)
• Simulating Quantum Materials with Digital Quantum Computers [55.41644538483948]
  Digital quantum computers (DQCs) can efficiently perform quantum simulations that are otherwise intractable on classical computers. The aim of this review is to provide a summary of progress made towards achieving physical quantum advantage.
  arXiv  Detail & Related papers  (2021-01-21T20:10:38Z)

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.