Quantum computing is said to be the next big technological leap and revolutionize the way in which information systems function and while this technology is perceived to still be in its infancy, the interest in it among researchers in all fields of science is growing rapidly. This paper presents the findings of a survey on recent developments in quantum machine learning in the field of speech acoustics, which is a field that has also experienced growing interest since 2020, but still has massive untapped scientific potential that will only increase as the technology progresses and will one day transition into massive improvement of commercial applications. These findings show that most existing studies in quantum speech acoustics rely on hybrid quantum-classical approaches, with very few fully quantum implementations due to current hardware constraints. Research efforts have largely concentrated on speech and emotion recognition, with healthcare-related applications also emerging as a promising area. The survey further reveals that while English dominates as the research language and Google Speech Commands is the most frequently used dataset, a diverse set of languages and corpora have been explored.
Quantum computing is said to be the next big technological leap and revolutionize the way in which information systems function and while this technology is perceived to still be in its infancy, the interest in it among researchers in all fields of science is growing rapidly. This paper presents the findings of a survey on recent developments in quantum machine learning in the field of speech acoustics, which is a field that has also experienced growing interest since 2020, but still has massive untapped scientific potential that will only increase as the technology progresses and will one day transition into massive improvement of commercial applications. These findings show that most existing studies in quantum speech acoustics rely on hybrid quantum-classical approaches, with very few fully quantum implementations due to current hardware constraints. Research efforts have largely concentrated on speech and emotion recognition, with healthcare-related applications also emerging as a promising area. The survey further reveals that while English dominates as the research language and Google Speech Commands is the most frequently used dataset, a diverse set of languages and corpora have been explored.
This survey report presents an overview of the field of quantum computing, focusing on the application of quantum technologies in machine learning, deep learning, image processing, and other related areas. A comprehensive analysis of research papers published between 2005 and 2022 is conducted, covering the latest advancements in quantum-based techniques. The report provides insights into various types of quantum technologies, including quantum machine learning, deep learning, computing, learning, and image processing. The survey encompasses a wide range of topics, such as quantum algorithms, quantum data encoding, quantum feature extraction, quantum neural networks, quantum image recognition, quantum optimization, and quantum simulations. It also identifies the emerging trends, challenges, and future research directions in this rapidly evolving field. The findings of this survey report are expected to provide valuable insights for researchers, practitioners, and decision-makers interested in the field of quantum and its applications in machine learning, deep learning, image processing, and related areas. Comprehensive analysis of the research papers and the identification of the current trends and challenges in this field can serve as a valuable reference for future research and development in the quantum computing domain.
Abstract This study explores Quantum AI’s potential in enhancing Speech Emotion Recognition (SER) systems. Our primary objective is to evaluate the performance of quantum-inspired algorithms compared to classical machine-learning approaches in accurately identifying and classifying emotions from speech signals. We hypothesise that quantum computing techniques can improve the efficiency and accuracy of emotion recognition, particularly in handling complex, highdimensional acoustic data. We developed a hybrid quantum-classical model that combines quantum-inspired neural networks with traditional feature extraction techniques. Our approach utilises quantum circuits for data encoding and quantum variational algorithms for classification. We implemented quantum versions of support vector machines (QSVM) and quantum approximate optimisation algorithms (QAOA) for emotion classification tasks. These quantum methods were compared against a hybrid Convolutional Neural Network and Long Short Term (LSTM). The hybrid network is called a Convolutional Long Short Term network(CLSTM). The study employed a custom dataset for speech emotion recognition. We prepossessed the audio data to extract relevant acoustic features, including mel-frequency cepstral coefficients (MFCCs), pitch, and energy. In contrast, our implemented quantum-inspired model for this corpus showed lower performance, with the highest training accuracy of 30%, struggling with most emotions but performing best with ’Trust’. These varied results across different implementations suggest that while Quantum AI techniques show promise in advancing speech emotion recognition, their effectiveness may depend on the specific dataset, language, and model architecture used. Finally, the speed of a simulated quantum network is shown to outperform current methods in terms of matrix multiplication by leaps and bounds.
Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the last three years. We address seven core research questions related to publication trends, the use of real quantum hardware versus simulators, quantum architectures overview, dataset types, medical domains, algorithmic frameworks, and reported results. Our analysis shows that most QML research in healthcare is conducted on simulators due to limited hardware access, and it relies on small datasets. Quantum convolutional neural network (QCNN) architectures dominate image-based medical tasks such as tumor detection, pneumonia diagnosis, and ECG interpretation, while feature-based datasets are mainly analyzed with variational quantum classifiers and quantum support vector machines. Despite hardware constraints, QML models often match or surpass classical machine learning approaches in accuracy, frequently reaching 95–99%. However, these performance statements should be qualified to recognize experimental limitations and avoid excessive optimism and should not be interpreted as definitive proof of quantum superiority at this stage. Additionally, issues with reproducibility and reporting of hardware details persist, which is a significant research gap. This review emphasizes the need for standardized benchmarks, more real hardware testing, and architecture-aware algorithm design. With the potential for accelerated diagnostics and personalized healthcare, QML represents a strategic direction for future medical research.
Quantum Machine Learning (QML) is evolving rapidly as improvements in quantum hardware, algorithmic theory, and hybrid quantum-classical software frameworks continue to unfold. Despite the high volume of published research, a set of unresolved challenges has motivated an increasingly critical examination of whether current approaches in QML are genuinely exploit the capabilities of quantum information processing. In this paper, a comprehensive narrative is developed that integrates the latest advances in quantum hardware, foundational principles of QML, methodological developments, present limitations, and emerging directions that will shape the near future of quantum learning. Drawing upon recent experimental milestones, theoretical studies, and applications across healthcare, environmental analytics, and molecular science, this manuscript clarifies where QML stands today and what is required for meaningful progress in the transition toward the fault-tolerant quantum era.
Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.
In the realm of artificial intelligence (AI), the integration of quantum computing with machine learning (ML) has sparked a significant shift in how we approach problems that were once deemed computationally infeasible. Quantum Machine Learning (QML) represents an exciting intersection of quantum mechanics and machine learning, potentially revolutionizing areas such as optimization, pattern recognition, and data analysis. Although quantum computing itself is still in its infancy, the synergy between quantum theory and classical machine learning algorithms promises to unlock new capabilities that classical computing could never achieve. This paper delves into the latest advancements in Quantum Machine Learning, its theoretical underpinnings, practical applications, and the challenges that researchers face as they explore this rapidly growing field.