Autoencoders are fundamental tools in classical computing for unsupervised feature extraction, dimensionality reduction, and generative learning. The Quantum Autoencoder (QAE), introduced by Romero J.[2017 Quantum Sci. Technol. 2 045001], extends this concept to quantum systems and has been primarily applied to tasks like anomaly detection. Despite its potential, QAE has not been extensively evaluated in basic classification tasks such as handwritten digit classification, which could provide deeper insights into its capabilities. In this work, we systematically investigate the performance of QAE in unsupervised feature extraction and reconstruction tasks. Using various encoder and decoder architectures, we explore QAE's ability to classify MNIST and analyze its reconstruction effectiveness. Notably, with a QCNN-based encoder, QAE achieves an average accuracy of 97.59% in binary classification of MNIST (0 and 1), demonstrating the feasibility of QAE for such tasks. This result highlights the synergy between QAE and QCNN in achieving optimal feature extraction performance. However, we also identify limitations in QAE's reconstruction capability, underscoring the need for further advancements in encoder and decoder designs. Our findings provide a foundation for future research on leveraging QAE in practical quantum machine learning applications.
Autoencoders are fundamental tools in classical computing for unsupervised feature extraction, dimensionality reduction, and generative learning. The Quantum Autoencoder (QAE), introduced by Romero J.[2017 Quantum Sci. Technol. 2 045001], extends this concept to quantum systems and has been primarily applied to tasks like anomaly detection. Despite its potential, QAE has not been extensively evaluated in basic classification tasks such as handwritten digit classification, which could provide deeper insights into its capabilities. In this work, we systematically investigate the performance of QAE in unsupervised feature extraction and reconstruction tasks. Using various encoder and decoder architectures, we explore QAE's ability to classify MNIST and analyze its reconstruction effectiveness. Notably, with a QCNN-based encoder, QAE achieves an average accuracy of 97.59% in binary classification of MNIST (0 and 1), demonstrating the feasibility of QAE for such tasks. This result highlights the synergy between QAE and QCNN in achieving optimal feature extraction performance. However, we also identify limitations in QAE's reconstruction capability, underscoring the need for further advancements in encoder and decoder designs. Our findings provide a foundation for future research on leveraging QAE in practical quantum machine learning applications.
Quantum convolutional neural networks (QCNNs) have become increasingly popular in quantum machine learning (QML) due to their efficient parameterization and hierarchical representation of quantum information. Anomaly detection is an important machine learning task with applications across a wide range of domains, including scientific data analysis. In this work, we adapt a QCNN architecture into a quantum autoencoder (QAE) framework for reconstruction-based anomaly detection. The models are trained in a semi-supervised manner on normal samples to reconstruct feature-extracted and dimensionally reduced time-series data, with reconstruction error used as an anomaly score. We investigate two quantum convolutional autoencoder architectures that differ in their treatment of latent information: a hierarchical architecture in which information remains distributed across the circuit and a bottleneck-based architecture in which information is explicitly compressed and reconstructed using additional decoder qubits. The size of the quantum latent space is varied to study its influence on reconstruction accuracy and anomaly detection performance. The approaches are benchmarked against both a variational quantum circuit and a comparable classical baseline using a real-world exoplanet anomaly-detection dataset. Results indicate a trade-off between latent-space size and model capacity, while also suggesting that explicit latent-space compression through a quantum bottleneck can improve anomaly detection performance relative to architectures that retain information throughout the circuit.
Quantum autoencoder (QAE) is a key tool in quantum machine learning and can be applied to areas such as dimensionality reduction and feature extraction [1]. Anomaly detection identifies deviations from normal patterns, which is crucial yet challenging due to the need to detect risks while minimizing false positives [2]. We propose a QAE-based anomaly detection scheme using an optical quantum neural network. Figure 1 (A) shows the basic structure of the QAE: the encoder compresses the input quantum state into a low-dimensional latent space, and the decoder reconstructs the original quantum state from the latent space. QAE-based anomaly detection works by learning the low-dimensional features of normal data and minimizing its reconstruction error. Normal data can be well reconstructed, while anomalous data exhibits a larger reconstruction error. When the reconstruction error of test data exceeds a set threshold, it is classified as anomalous. We constructed the QAE using an optical quantum neural network consisting of 50:50 beam splitters and controllable phase shifters [3]. Due to the unitary matrix property, only the encoder needs to be trained, and the decoder is simply the conjugate transpose of the encoder. To compress an N-dimensional quantum stateρintoKdimensions, efficient compression can be achieved by minimizing the expectation value of the projection onto the trash state, given byC(θ,ϕ)=Tr(PU(θ,ϕ)ρU+(θ,ϕ))[4]. Here,P=(IK⊕∣0⟩⊗N−K⟨0∣⊗(N−K))represents the projection onto the trash state,IKis the K-dimensional identity matrix,∣0⟩⊗(N−K)isthe trash state,Trdenotes the trace,U(θ,ϕ)is the encoder with adjustable parameters, andU†(θ,ϕ)is the decoder. We only need to train the encoder during the training process, and the decoder is simply its conjugate transpose.
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in quantum machine learning (QML). Among QML techniques, quantum autoencoders show promise for compressing high-dimensional quantum and classical data. However, designing effective quantum circuit architectures for quantum autoencoders remains challenging due to the complexity of selecting gates, arranging circuit layers, and tuning parameters.
This paper proposes a neural architecture search (NAS) framework that automates the design of quantum autoencoders using a genetic algorithm (GA). By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders for data reconstruction without becoming trapped in local minima. We demonstrate effectiveness on image datasets, highlighting the potential of quantum autoencoders for efficient feature extraction within a noise-prone, near-term quantum era. Our approach lays a foundation for broader application of genetic algorithms to quantum architecture search, aiming for a robust, automated method that can adapt to varied data and hardware constraints.
Quantum Auto-Encoders (QAEs) represent a transformative approach to anomaly detection, leveraging quantum computational power to address challenges in domains such as healthcare and finance. In this study, we focus on detecting anomalies in breast cancer diagnosis and fraudulent financial transactions. QAEs are trained exclusively on non-anomalous data, enabling anomaly detection through their inability to faithfully reconstruct anomalous points. Reconstruction quality is evaluated using fidelity tests for simulations and SWAP tests for quantum hardware, ensuring reliable and robust performance across diverse environments. Our work introduces QEAFAD: Quantum Enhanced Autoencoders for Anomaly Detection, incorporating enhanced and patch-based architectures that demonstrate significant advancements in precision. By integrating quantum entanglement into the encoding and decoding processes, via shared Bell pairs and direct qubit entanglement. Our framework improves correlations and delivers statistically significant performance gains. Benchmarks on the MNIST dataset validate the scalability of our approach, while JAX-accelerated optimization reduces training time on financial datasets. Experiments on IBM and Amazon quantum hardware confirm the practical feasibility of QAEs. These advancements highlight the efficiency of QAEs, achieving superior accuracy with reduced computational resources, making them a compelling solution for real-world anomaly detection challenges in critical applications.
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multilayer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in quantum machine learning (QML). Among QML techniques, quantum autoencoders show promise for compressing highdimensional quantum and classical data. However, designing effective quantum circuit architectures for quantum autoencoders remains challenging due to the complexity of selecting gates, arranging circuit layers, and tuning parameters. This paper proposes a neural architecture search (NAS) framework that automates the design of quantum autoencoders using a genetic algorithm (GA). By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders for data reconstruction without becoming trapped in local minima. We demonstrate effectiveness on image datasets, highlighting the potential of quantum autoencoders for efficient feature extraction within a noise-prone, near-term quantum era. Our approach lays a foundation for broader application of genetic algorithms to quantum architecture search, aiming for a robust, automated method that can adapt to varied data and hardware constraints.
This research explores quantum machine learning for image classification, comparing classical and quantum models. A classical convolutional neural network (CNN), based on the LeNet architecture, achieved 99.12% accuracy on MNIST, outperforming a quantum CNN, which attained 93.30%. Quantum encoders applied to Fashion-MNIST reached 85.60%, while quantum support vector machine (SVM), implemented for binary classification, obtained 71.34%. The study integrates hybrid quantum-classical models, leveraging quantum circuits for feature extraction. A quantum autoencoder-based classifier was introduced, demonstrating efficient data compression and classification without additional qubits. Feature encoding using RX gates mapped classical data into quantum states, and the impact of parameterized quantum circuit structures on classification accuracy was analyzed. Results indicate that specific ansatz structures yield superior accuracy, highlighting the significance of quantum circuit design. While classical models currently outperform quantum counterparts, quantum approaches reduce trainable parameters and computational complexity. Limitations in current quantum hardware, including noise and scalability, restrict performance. However, advancements in error mitigation, quantum gate efficiency, and scalable quantum architecture could bridge the gap with classical methods. These quantum techniques hold potential in aerospace applications, such as satellite image classification, anomaly detection in remote sensing, and real-time processing of high-dimensional image data. This study underscores the growing role of quantum circuits in machine learning, particularly in feature extraction and classification, suggesting that future quantum hardware could unlock new capabilities in end-to-end learning beyond hybrid approaches.