In the NISQ era, exploring quantum approaches capable of addressing real-world data analytics tasks has been a focal point of research. The detection of anomalous images is one of the classic data processing problems that can be considered to tackle using quantum computing. Inspired by the proposal of quantum circuit autoencoders (QCAE), we design a quantum method to tackle image anomaly detection tasks. In this work, we propose a method for encoding an image into a quantum circuit. Then, we employ the QCAE model to detect the anomalous images. Moreover, we create a scoring function to predict whether test data is an outlier. Finally, combining the trained QCAE with the scoring function, we obtain an anomaly detector. We use the handwritten digit image dataset to train detectors under various training setups and validate their performance on a test set. Our method achieves an f1-score of 0.8190 and a recall of 0.8600. The experimental results demonstrate the potential of quantum computing in handling data anomalies in the classical world, encouraging further exploration of QCAE applications in practical problems.
In the NISQ era, exploring quantum approaches capable of addressing real-world data analytics tasks has been a focal point of research. The detection of anomalous images is one of the classic data processing problems that can be considered to tackle using quantum computing. Inspired by the proposal of quantum circuit autoencoders (QCAE), we design a quantum method to tackle image anomaly detection tasks. In this work, we propose a method for encoding an image into a quantum circuit. Then, we employ the QCAE model to detect the anomalous images. Moreover, we create a scoring function to predict whether test data is an outlier. Finally, combining the trained QCAE with the scoring function, we obtain an anomaly detector. We use the handwritten digit image dataset to train detectors under various training setups and validate their performance on a test set. Our method achieves an f1-score of 0.8190 and a recall of 0.8600. The experimental results demonstrate the potential of quantum computing in handling data anomalies in the classical world, encouraging further exploration of QCAE applications in practical problems.
Abstract Anomaly detection in cybersecurity is a challenging task, where normal events far outnumber anomalous ones with new anomalies occurring frequently. Classical autoencoders have been used for anomaly detection, but struggles in data-limited settings which quantum counterparts can potentially overcome. In this work, we apply Quantum Autoencoders (QAEs) for anomaly detection in cybersecurity, specifically on the BPF-extended tracking honeypot (BETH) dataset. QAEs are evaluated across multiple encoding techniques, ansatz types, repetitions, and feature selection strategies. Our results demonstrate that an 8-feature QAE using Dense-Angle encoding with a RealAmplitude ansatz can outperform Classical Autoencoders (CAEs), even when trained on substantially fewer samples. The effects of quantum encoding and feature selection for developing quantum models are demonstrated and discussed. In a data-limited setting, the best performing QAE model has a F1 score of 0.87, better than that of CAE (0.77). These findings suggest that QAEs may offer practical advantages for anomaly detection in data-limited scenarios.
A quantum autoencoder is a quantum neural network model for compressing information stored in quantum states. However, one needs to process information stored in quantum circuits for many tasks in the emerging quantum information technology. In this work, generalizing the ideas of classical and quantum autoencoders, we introduce the quantum circuit autoencoder (QCAE) model to compress and encode information within quantum circuits. We provide a comprehensive protocol for QCAE and design a variational quantum algorithm, varQCAE, for its implementation. We theoretically analyze this model by deriving conditions for lossless compression and establishing both upper and lower bounds on its recovery fidelity. Finally, we apply varQCAE to three practical tasks, and numerical results show that it can effectively (1) compress the information within quantum circuits, (2) detect anomalies in quantum circuits, and (3) mitigate the depolarizing noise in quantum circuits. These suggest that our algorithm is potentially applicable to other information processing tasks for quantum circuits.
Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition or medical diagnosis. Several algorithms have been introduced using classical computing approaches. However, using quantum computing for solving anomaly detection problems in time series data is a widely unexplored research field. This paper explores the application of quantum autoencoders to time series anomaly detection. We investigate two primary techniques for classifying anomalies: (1) Analyzing the reconstruction error generated by the quantum autoencoder and (2) latent representation analysis. Our simulated experimental results, conducted across various ansaetze, demonstrate that quantum autoencoders consistently outperform classical deep learning-based autoencoders across multiple datasets. Specifically, quantum autoencoders achieve superior anomaly detection performance while utilizing 60-230 times fewer parameters and requiring five times fewer training iterations. In addition, we implement our quantum encoder on real quantum hardware. Our experimental results demonstrate that quantum autoencoders achieve anomaly detection performance on par with their simulated counterparts.
In this study, a parameterized quantum circuit (PQC) is applied for anomaly detection, a crucial process to identify unusual patterns or outliers in data. PQC is a quantum circuit with trainable parameters linked to quantum gates, which are iteratively optimized by classical optimizers to ensure that the circuit’s output fulfills its objectives. This is analogous to the way of using trainable parameters, such as weights adjusted in classical machine learning and neural network models. We used the amplitude−embedding mechanism with classical data into quantum states of qubits. These states are fed into PQC, which contains strongly entangled layers, and the circuit is trained to determine whether an anomaly exists. As anomaly detection datasets are often imbalanced, resampling techniques, such as random oversampling, the synthetic minority oversampling technique (SMOTE), random undersampling, and Tomek-Link undersampling, are applied to reduce the imbalance. The proposed PQC and various resampling techniques were compared using the public Musk dataset for anomaly detection. Their combination was also compared with the combination of the classical autoencoder and the classical isolation forest model in terms of the F1 score. By analyzing the comparison results, the advantages and disadvantages of PQC for future research studies were determined.
Abstract Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition, or medical diagnosis. Several algorithms have been introduced using classical computing approaches. However, using quantum computing for solving anomaly detection problems in time series data is a widely unexplored research field. This paper explores the application of quantum autoencoders to time series anomaly detection. We investigate two primary techniques for classifying anomalies: (1) Analyzing the reconstruction error generated by the quantum autoencoder and (2) latent representation analysis. Our simulated experimental results, conducted across various ansaetze, demonstrate that quantum autoencoders consistently outperform classical deep learning-based autoencoders across multiple datasets. Specifically, quantum autoencoders achieve superior anomaly detection performance while utilizing 60–230 times fewer parameters and requiring five times fewer training iterations. In addition, we implement our quantum encoder on real quantum hardware. Our experimental results demonstrate that quantum autoencoders achieve anomaly detection performance on par with their simulated counterparts.
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.