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A Quantum-Classical Hybrid Solution for Deep Anomaly Detection.

Maida Wang1, Anqi Huang2, Yong Liu2

  • 1School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.

Entropy (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

This study introduces a novel quantum machine learning (QML) approach for deep image anomaly detection (AD). The quantum-classical hybrid deep neural network (QHDNN) demonstrates feasibility and superior performance compared to classical methods.

Keywords:
deep learningimage anomaly detectionquantum hybrid deep neural networkquantum machine learning

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Computer Vision

Background:

  • Deep anomaly detection (AD) is crucial for identifying outliers in data.
  • Image AD, a key area of deep AD, utilizes deep neural networks (DNNs).
  • Quantum machine learning (QML) is an emerging field with significant potential.

Purpose of the Study:

  • To propose a novel quantum-classical hybrid deep neural network (QHDNN) for deep image AD.
  • To investigate the efficacy of QML in addressing the challenges of image anomaly detection.
  • To develop a QHDNN capable of learning normality from raw images and detecting anomalies.

Main Methods:

  • Designed a quantum-classical hybrid DNN (QHDNN) architecture.
  • Explored various quantum layer designs, focusing on a variational quantum circuit (VQC)-based QHDNN.
  • Trained the QHDNN on normal images to establish a normality model for anomaly detection.

Main Results:

  • Demonstrated the feasibility of using QML for deep image AD through extensive experiments.
  • The proposed QHDNN achieved competitive and, in some cases, superior performance compared to classical counterparts with similar parameter counts.
  • Validated the QHDNN's ability to effectively discriminate between normal and anomalous images.

Conclusions:

  • QML offers a promising avenue for advancing deep image anomaly detection.
  • Quantum-classical hybrid models can potentially outperform traditional deep learning methods in image AD tasks.
  • The developed VQC-based QHDNN presents a viable and effective solution for deep image AD.