Multi-qubit state visualizations to support problem solving $-$ a pilot study

• URL: http://arxiv.org/abs/2406.16556v1
• Date: Mon, 24 Jun 2024 11:46:35 GMT
• Title: Multi-qubit state visualizations to support problem solving $-$ a pilot study
• Authors: Jonas Bley, Eva Rexigel, Alda Arias, Lars Krupp, Steffen Steinert, Nikolas Longen, Paul Lukowicz, Stefan Küchemann, Jochen Kuhn, Maximilian Kiefer-Emmanouilidis, Artur Widera,
• Abstract summary: We compare students' performance, time taken, and cognitive load when solving problems using the mathematical-symbolic Dirac notation alone with using it accompanied by the circle notation or the dimensional circle notation in single- and multi-qubit systems. Although little overall differences in students' performance can be detected depending on the presented representations, we observe that problem-solving performance is student- and context-dependent.
• Score: 1.8879980022743639
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In the rapidly evolving interdisciplinary field of quantum information science and technology, a big obstacle is the necessity of understanding high-level mathematics to solve complex problems. Visualizations like the (dimensional) circle notation enable us to visualize not only single-qubit but also complex multi-qubit states, entanglement, and quantum algorithms. Current findings in educational research suggest that incorporating visualizations in settings of problem solving can have beneficial effects on students' performance and cognitive load compared to solely relying on symbolic problem solving content. In this pilot study, we aim to take a first step to identify in which contexts students benefit from the presentation of visualizations of multi-qubit systems in addition to mathematical formalism. We compare students' performance, time taken, and cognitive load when solving problems using the mathematical-symbolic Dirac notation alone with using it accompanied by the circle notation or the dimensional circle notation in single- and multi-qubit systems. Although little overall differences in students' performance can be detected depending on the presented representations, we observe that problem-solving performance is student- and context-dependent. In addition, the results indicate reduced cognitive load when participants are presented with visualization. The results are discussed with respect to relevant design aspects for future studies.

Related papers

• VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM [0.5383910843560784]
  This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem's core mathematical concepts, improving both problem creation and assessment.
  arXiv  Detail & Related papers  (2024-11-08T09:15:56Z)
• A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning [5.22145960878624]
  The graph partitioning problem is recognized as an NP-hard prob-lem. We introduce a novel pipeline employing an unsupervised graph neural network to solve the graph partitioning problem. We rigor-ously evaluate our methodology against contemporary state-of-the-art tech-niques, focusing on metrics: cuts and balance, and our findings reveal that our is competitive with these leading methods.
  arXiv  Detail & Related papers  (2023-12-11T23:03:17Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning [98.78136504619539]
  Causal Triplet is a causal representation learning benchmark featuring visually more complex scenes. We show that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts.
  arXiv  Detail & Related papers  (2023-01-12T17:43:38Z)
• Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization [6.077274947471846]
  We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE) GRATE selects student attempts that can be aggregated while predicting students' performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-10-06T06:22:46Z)
• Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem [60.0878532426877]
  We propose a novel collaborative learning scheme from the viewpoint of visual perturbation calibration. Specifically, we devise a visual controller to construct two sorts of curated images with different perturbation extents. The experimental results on two diagnostic VQA-CP benchmark datasets evidently demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2022-07-24T23:50:52Z)
• Causal Reasoning Meets Visual Representation Learning: A Prospective Study [117.08431221482638]
  Lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. Inspired by the strong inference ability of human-level agents, recent years have witnessed great effort in developing causal reasoning paradigms. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods.
  arXiv  Detail & Related papers  (2022-04-26T02:22:28Z)
• Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification [50.83222170524406]
  We study the intrinsic difficulty in graph classification under the unified concept of resolution dilemmas'' We propose SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling.
  arXiv  Detail & Related papers  (2020-06-29T01:01:42Z)
• Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning [95.18337034090648]
  We propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG) These visual arithmetic problems are in the form of geometric figures. We benchmark the MNS dataset using four predominant neural network models as baselines in this visual reasoning task.
  arXiv  Detail & Related papers  (2020-04-25T17:14:58Z)

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.