Developing systems capable of compressing audio files is a very appealing research topic. This is due to the necessity to improve storage utilisation and accelerate data transfer over restricted communication channels. As a result, numerous investigators have explored and developed many methods to compress audio information with several approaches; everyone has negative consequences, such as excessive time consumption or complex computations, and remains a crucial problem. Recent improvements in deep learning have prompted researchers to use unified deep network models to investigate challenges needing highly organised data. The building and design of such models for compressing audio signals have been problematic due to the demand for discrete representations that are difficult to train. This research focuses on quantum neural networks for audio file compression. Our method uses an innovative encoding process that embeds audio signals in quantum states. Our framework can compress larger files than previously possible for the BBC sound dataset while achieving a better compression ratio than classical neural networks.
Developing systems capable of compressing audio files is a very appealing research topic. This is due to the necessity to improve storage utilisation and accelerate data transfer over restricted communication channels. As a result, numerous investigators have explored and developed many methods to compress audio information with several approaches; everyone has negative consequences, such as excessive time consumption or complex computations, and remains a crucial problem. Recent improvements in deep learning have prompted researchers to use unified deep network models to investigate challenges needing highly organised data. The building and design of such models for compressing audio signals have been problematic due to the demand for discrete representations that are difficult to train. This research focuses on quantum neural networks for audio file compression. Our method uses an innovative encoding process that embeds audio signals in quantum states. Our framework can compress larger files than previously possible for the BBC sound dataset while achieving a better compression ratio than classical neural networks.
Recent work suggests that quantum machine learning techniques can be used for classical image classification by encoding the images in quantum states and using a quantum neural network for inference. However, such work has been restricted to very small input images, at most 4 x 4, that are unrealistic and cannot even be accurately labeled by humans. The primary difficulties in using larger input images is that hitherto-proposed encoding schemes necessitate more qubits than are physically realizable. We propose a framework to classify larger, realistic images using quantum systems. Our approach relies on a novel encoding mechanism that embeds images in quantum states while necessitating fewer qubits than prior work. Our framework is able to classify images that are larger than previously possible, up to 16 x 16 for the MNIST dataset on a personal laptop, and obtains accuracy comparable to classical neural networks with the same number of learnable parameters. We also propose a technique for further reducing the number of qubits needed to represent images that may result in an easier physical implementation at the expense of final performance. Our work enables quantum machine learning and classification on classical datasets of dimensions that were previously intractable by physically realizable quantum computers or classical simulation
Abstract The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle. We introduce a novel model architecture that combines classical convolutional layers with a quantum neural network, aiming to surpass state-of-the-art accuracy while maintaining a compact model size. The experiment is to classify high-dimensional audio data from the Bird-CLEF 2021 dataset. Our evaluation focuses on key metrics, including training duration, model accuracy, and total model size. This research demonstrates the promising potential of quantum machine learning in enhancing machine learning tasks and solving practical machine learning challenges available today.
Audio classification is a key application in deep learning, and quantum machine learning (QML) provides new possibilities by capturing complex patterns in signals through parameterized quantum circuits. However, existing qubit-based models struggle to efficiently represent continuous-valued data common in audio processing, limiting their scalability and expressivity. Continuous Variable Quantum Computing (CVQC) offers a natural framework for processing real-valued data such as mel-spectrograms, addressing the limitations of qubit-based systems while introducing nonlinearity through non-gaussian gates to ensure universality. In CVQC, each qumode represents an infinite-dimensional Hilbert space, enabling compact yet expressive information representation. This work introduces a two-qumode Hybrid Continuous Variable Quantum Neural Network (HCVQNN) for audio classification that integrates classical neural layers with a CV quantum circuit for advanced feature extraction and encoding. Experiments conducted using PennyLane demonstrate that HCVQNN achieves comparable audio classification accuracy while requiring fewer resources compared to classical deep networks and qubit-based quantum models.
Audio signals being high-dimensional and complex pose challenges for classical machine learning techniques in terms of computation and generalization on real-world data. This paper evaluates the use of hybrid quantum-classical convolutional neural networks (QC-CNN) that leverage quantum effects like superposition and entanglement for audio classification using mel-spectrograms obtained from audio data. Evaluated on both small-sized and large-sized datasets, the proposed QC-CNN model gave comparable training accuracy with classical CNN (Convolutional Neural Network) on the smaller dataset but outperformed classical CNN on test accuracy (95.04% vs 92.88%) for a larger birdsong dataset and reduced overfitting, thus highlighting the potential advantages of QC-CNNs for audio data. The QC-CNN exhibited higher cross-entropy loss in case of the small-sized dataset which was further significantly reduced when evaluated on the large-sized birdsong dataset. The work demonstrates the application of QC-CNN for audio classification.
Quantum neural networks (QNNs) have recently attracted remarkable attention as a potential alternative to classical and deep neural network (NN) models particularly under constraints imposed by noisy intermediate-scale quantum (NISQ) devices, which support only shallow circuits with few quantum bits (qubits) and limited variational depth. However, applying QNNs to natural language processing (NLP) remains challenging because text representations are typically high-dimensional, while NISQ classifiers can only encode a small number of features at a time, requiring classical compression to enable stable training and flexible circuit configuration. In addition, many existing studies integrate QNNs into rich-text representation pipelines, where the quantum circuit is often implemented as a variational quantum circuit (VQC). Here, the VQC acts as an additional component within a larger model, and it remains unclear how the quantum component improves the classification task. Therefore, in this work, we propose a fully reproducible and compact QNN pipeline that provides a controlled setting for studying the contribution of the VQC to text classification. The model is trained end-to-end with a lightweight classical compression layer, and the overall study is supported by systematic ablation analyses of the compression layer, the classifier heads under a shared frozen low-dimensional representation, and quantum circuit structure. Our model uses the term frequency-inverse document frequency (TF-IDF) features with linear compression followed by a shallow four-qubit, two-layer VQC, implemented in PyTorch and PennyLane for end-to-end training. It is evaluated on five benchmark text classification datasets against classical baselines, including logistic regression and multilayer perceptron (MLP) models. Experimental results show that the proposed QNN achieves a competitive performance across all evaluated datasets, yielding the best results on three of the five benchmark datasets.
Quantum computing is described as a process by which a system calculates output. Quantum physics usually refers to the smallest discrete unit of any property; the basic unit of data in quantum is the qubit; this qubit unit is equivalent to the bit unit in classical neural networks. Quantum deep learning combines quantum computing with deep learning to reduce training time for neural networks, which has proven effective in solving some intractable problems on classical computers. Quantum deep learning has proven effective in solving some intractable problems on classical computers. A quantum network can benefit from quantum informationflow because it is a more efficient framework than classical systems. Each quantum deep learning consists of a quantum gate. In this review, we provide a comprehensive review of recent studies that include different quantum deep learning applications, including healthcare, handwriting, and many others. Also, methodologies, problems, main datasets, results, strengths, limitations, and challenges are included in this review.