Quantum Computing in the Arts and Humanities: An Introduction to Core Concepts, Theory and Applications

Abstract

Intro -- Preface -- Contents -- From Digital Humanities to Quantum Humanities: Potentials and Applications -- 1 Introduction -- 2 Towards Quantum Humanities -- 2.1 Digital Humanities -- 2.2 Potential Benefits of Quantum Humanities -- 2.3 Current Challenges of Quantum Humanities -- 3 Quantum Humanities Use Case: Project MUSE -- 3.1 MUSE Ontology -- 3.2 MUSE Film Corpus -- 3.3 MUSE Data Set -- 3.4 MUSE Data Analysis -- 3.5 MUSE Costume Patterns -- 4 Analysing Data -- 4.1 Data Analysis Pipeline -- 4.2 Categorical Data -- 4.3 Creating Pattern Languages Based on Data Analysis -- 5 Artificial Neural Networks -- 5.1 Neurons -- 5.2 Neural Networks -- 5.3 Perceptrons -- 5.4 Restricted Boltzmann Machines -- 5.5 Autoencoders -- 6 Variational Hybrid Quantum-Classical Algorithms -- 6.1 The Main Idea -- 6.2 Maximum Cut: A Combinatorial Optimization Problem -- 6.3 QAOA -- 6.4 Computing Maximum Cuts via Eigenvalues -- 6.5 VQE -- 7 QHAna: A Quantum Humanities Analysis Tool -- 7.1 Support the Identification of (Costume) Patterns -- 7.2 Comparing Classical and Quantum Machine Learning Algorithms -- 7.3 Improve the Understanding of the Benefits of Quantum Computing -- 7.4 Integration of Heterogeneous Tools -- 7.5 Provide Easy Access to Quantum Computing -- 8 Conclusion and Outlook -- References -- Quantum Computing and Cognitive Simulation -- 1 Introduction -- 1.1 Mathematical Notation -- 2 Cognitive Phenomena -- 2.1 Asymmetry of Similarity Judgements -- 2.2 Diagnosticity -- 2.3 Conjunction and Disjunction Fallacies -- 2.4 Question Order Models -- 2.5 The `Sure Thing' Principle -- 2.6 Categorization -- 2.7 Negation -- 3 Quantum Models -- 3.1 Overview of Quantum Models in Psychology -- 3.2 Similarity Judgements -- 3.3 Diagnosticity -- 3.4 Conjunction Fallacy and Over/Underextension -- 3.5 Violation of the `Sure Thing' Principle.