Quantum Patch-Based Autoencoder for Anomaly Segmentation

• URL: http://arxiv.org/abs/2404.17613v1
• Date: Fri, 26 Apr 2024 08:42:58 GMT
• Title: Quantum Patch-Based Autoencoder for Anomaly Segmentation
• Authors: Maria Francisca Madeira, Alessandro Poggiali, Jeanette Miriam Lorenz,
• Abstract summary: We introduce a patch-based quantum autoencoder (QPB-AE) for image anomaly segmentation. QPB-AE reconstructs the quantum state of the embedded input patches, computing an anomaly map directly from measurement. We evaluate its performance across multiple datasets and parameter configurations.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both supervised and unsupervised methods. Autoencoders are commonly used in unsupervised tasks, where models are trained to reconstruct normal instances efficiently, allowing anomaly identification through high reconstruction errors. While quantum autoencoders have been proposed in the literature, their application to anomaly segmentation tasks remains unexplored. In this paper, we introduce a patch-based quantum autoencoder (QPB-AE) for image anomaly segmentation, with a number of parameters scaling logarithmically with patch size. QPB-AE reconstructs the quantum state of the embedded input patches, computing an anomaly map directly from measurement through a SWAP test without reconstructing the input image. We evaluate its performance across multiple datasets and parameter configurations and compare it against a classical counterpart.

Related papers

• Applying Quantum Autoencoders for Time Series Anomaly Detection [1.4732811715354452]
  Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition or medical diagnosis. This paper explores the application of quantum autoencoders to time series anomaly detection.
  arXiv  Detail & Related papers  (2024-10-05T13:29:25Z)
• Quantum support vector data description for anomaly detection [0.5439020425819]
  Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection.
  arXiv  Detail & Related papers  (2023-10-10T07:35:09Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Variational quantum one-class classifier [4.350783459690612]
  One-class classification is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm for such a problem, which we call a variational quantum one-class classifier (VQOCC) The performance of the VQOCC is compared with that of the one-class support vector machine (OC-SVM), the kernel principal component analysis (PCA), and the deep convolutional autoencoder (DCAE) using handwritten digit and Fashion-MNIST datasets.
  arXiv  Detail & Related papers  (2022-10-06T04:32:55Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• Learning Representations for CSI Adaptive Quantization and Feedback [51.14360605938647]
  We propose an efficient method for adaptive quantization and feedback in frequency division duplexing systems. Existing works mainly focus on the implementation of autoencoder (AE) neural networks for CSI compression. We recommend two different methods: one based on a post training quantization and the second one in which the codebook is found during the training of the AE.
  arXiv  Detail & Related papers  (2022-07-13T08:52:13Z)
• Measuring NISQ Gate-Based Qubit Stability Using a 1+1 Field Theory and Cycle Benchmarking [50.8020641352841]
  We study coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application. We identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor. This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
  arXiv  Detail & Related papers  (2022-01-08T23:12:55Z)
• Tree tensor network classifiers for machine learning: from quantum-inspired to quantum-assisted [0.0]
  We describe a quantum-assisted machine learning (QAML) method in which multivariate data is encoded into quantum states in a Hilbert space whose dimension is exponentially large in the length of the data vector. We present an approach that can be implemented on gate-based quantum computing devices.
  arXiv  Detail & Related papers  (2021-04-06T02:31:48Z)
• Supervised Learning Using a Dressed Quantum Network with "Super Compressed Encoding": Algorithm and Quantum-Hardware-Based Implementation [7.599675376503671]
  Implementation of variational Quantum Machine Learning (QML) algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices has issues related to the high number of qubits needed and the noise associated with multi-qubit gates. We propose a variational QML algorithm using a dressed quantum network to address these issues. Unlike in most other existing QML algorithms, our quantum circuit consists only of single-qubit gates, making it robust against noise.
  arXiv  Detail & Related papers  (2020-07-20T16:29:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.