The benefits of using classical computers in the humanities has been proven by the establishment of the digital humanities. Recently, quantum computers reached a state in which advantages can be realized in certain use cases. E.g., initial feasibility studies in the humanities have shown that some advantages can already be achieved, and much more can be expected, especially in applying quantum machine learning. But building quantum applications requires a deep understanding of mathematics, quantum algorithms and programming that cannot be expected by humanists. Consequently, they cannot explore potentially promising applications or apply concrete potentials of quantum algorithms. The proposed architecture and method aim at exactly this, enabling (digital) humanists to easily build their own quantum applications in the focus area of quantum machine learning to allow a broad use and critical evaluation of the potentials of quantum algorithms for humanities research.
The benefits of using classical computers in the humanities has been proven by the establishment of the digital humanities. Recently, quantum computers reached a state in which advantages can be realized in certain use cases. E.g., initial feasibility studies in the humanities have shown that some advantages can already be achieved, and much more can be expected, especially in applying quantum machine learning. But building quantum applications requires a deep understanding of mathematics, quantum algorithms and programming that cannot be expected by humanists. Consequently, they cannot explore potentially promising applications or apply concrete potentials of quantum algorithms. The proposed architecture and method aim at exactly this, enabling (digital) humanists to easily build their own quantum applications in the focus area of quantum machine learning to allow a broad use and critical evaluation of the potentials of quantum algorithms for humanities research.
The emergence and advancement of new technologies present promising opportunities for data-driven research in the Digital Humanities (DH). As an innovative intersection of quantum computing and humanities data, Quantum Humanities (QH) holds significant research potential. However, current studies related to QH remain scarce with limited applications involving quantum deep learning, and its feasibility, effectiveness, and efficiency on humanities data need in-depth exploration. To address this gap, this paper takes the role type recognition of painted faces of Beijing Opera as a case study. Several classical and competitive quantum deep learning–based and classical deep learning–based models (e.g., quantum convolutional neural networks and classical convolutional neural networks) are selected for comparison on the public image dataset Painted Faces of Beijing Opera digitized from books. Extensive empirical experimental results demonstrate that quantum deep learning–based models are of certain advantages and hold promising prospects in applied DH practices.
Quantum Machine Learning (QML) represents a promising frontier of research where the principles of quantum mechanics are combined with machine learning to offer potentially superior performance over classical counterparts. This review article explores the current advancements in QML, specifically focusing on the performance improvement and comparison between standard classical algorithms and their quantum implementations. We initiate with an overview of fundamental quantum algorithms that have found applicability within machine learning contexts, such as quantum principal component analysis and quantum support vector machines. The review then delves into a critical assessment of these quantum approaches, highlighting case studies where quantum implementations have demonstrated computational speedups or improved accuracy. Furthermore, we investigate the inherent challenges and limitations that currently hinder the widespread adoption of QML technologies, including the issues of decoherence, error rates, and quantum resource requirements. In addition, the review provides insights into the current state of quantum hardware and its scalability for machine learning tasks. Throughout, comparisons are made to outline scenarios in which quantum machine learning presents a genuine advantage over classical techniques, supported by performance metrics and benchmark datasets. The article concludes by discussing future directions in QML research, with an emphasis on algorithmic innovation and hardware improvement, to realize the full potential of quantum-enhanced machine learning.
Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields.
Abstract Quantum Machine Learning has emerged as a promising field at the intersection of Quantum Computing and Artificial Intelligence, offering the potential to achieve computational advantages over classical machine learning approaches. While classical ML has matured into a robust ecosystem with broad applications, QML remains nascent, constrained by hardware limitations, yet fueled by rapid theoretical and algorithmic developments. This paper presents a comprehensive comparative review of QML and classical ML, focusing on their foundations, complexity, and applications across domains. We analyze where QML models have shown advantages, where they lag behind, and try to provide possible explanations for these results. By synthesizing evidence across neural networks, kernel methods, generative models, representation learning, and clustering, we highlight patterns that indicate when quantum models tend to outperform or underperform classical baselines. We also review real-world applications of QML across diverse domains such as materials science, energy forecasting, healthcare and bioinformatics, transportation, and finance. Finally, we identify open challenges including state preparation, scalability, and benchmarking standards, and propose a roadmap for the development of QML into a field with demonstrable advantages.
Quantum Machine Learning (QML) is an emerging interdisciplinary field at the intersection of quantum computing and machine learning, delivering new era for advancing learning beyond the limitations of conventional computational systems. By leveraging quantum computing, QML introduces fundamentally different representational spaces that may support novel learning dynamics, expressive model architectures, and potential algorithmic advantages. This workshop explores how quantum-based approaches can be meaningfully integrated into modern machine learning and computer graphics pipelines. Rather than positioning quantum computing as a distant theoretical concept, the workshop frames it as an emerging computational substrate with practical relevance for hybrid architectures, quantum-enhanced models, and future learning paradigms. Meanwhile, the workshop will also examine near-term quantum hardware, deployable system design, and rigorous evaluation methods for distinguishing genuine quantum advantage from strong classical baselines. By bringing together researchers from quantum computing, machine learning, and computer graphics, this workshop aims to bridge theory and practice, identify near-term application opportunities, and foster interdisciplinary discussion on the future of scalable and practical QML.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry, and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.