Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE International Conference on Smart Computing Workshops and Other Affiliated Events (SMARTCOMP Companion)

Assessing the Feasibility of Quantum Autoencoders for Real-World Visual Anomaly Detection

Farida Farsian, Attilio Azzarelli, Fabrizio De Vita, Giovanni Merlino, Francesco Schillirò

Published Jun 1, 2026• 0 citations
Publisher
DOI
10.1109/smartcomp-companion70724.2026.00032

Related papers

The role of data embedding in quantum autoencoders for improved anomaly detection

Jack Y. Araz, Michael Spannowsky

arXiv:2409.04519•Quantum Machine Intelligence•Jun 1, 2026•2 citations

Hybrid classical-quantum autoencoder for anomaly detection

Alona Sakhnenko, Corey O’Meara, Kumar Ghosh +3 more

arXiv:2112.08869•Quantum Machine Intelligence•Sep 22, 2022•47 citations

Self-supervised Deep Learning for Real-Time Anomaly Detection in Quantum Sensor Networks

Lakshmojee Koduru, Monu Sharma, Krishna Charan Bajanthri Chikalaguriki +3 more

Lecture notes in networks and systems•Nov 6, 2025•0 citations

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel

We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model training, the approach transforms localized learning on edge devices without requiring transmission of raw data, thereby preserving privacy and minimizing communication overhead. The model leverages quantum advantage in pattern recognition to enhance detection sensitivity, particularly in complex and dynamic IoT network traffic. Experiments on a real-world IoT dataset show that the proposed method delivers anomaly detection accuracy and robustness comparable to centralized approaches, while ensuring data privacy.

quant-pharXiv:2603.22366•Mar 24, 2026

Self-supervised Deep Learning for Real-Time Anomaly Detection in Quantum Sensor Networks

Lakshmojee Koduru, Monu Sharma, Krishna Charan Bajanthri Chikalaguriki +3 more

Lecture notes in networks and systems•Nov 6, 2025•0 citations

QEA-FAD: Quantum Enhanced Autoencoders for Anomaly Detection in Finance and Healthcare

Sourodeep Kundu, Deepti Mishra, Nachiketa Tarasia +2 more

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.

International Conference on Networks and Cryptology (NETCRYPT)•May 29, 2025•2 citations
We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model training, the approach transforms localized learning on edge devices without requiring transmission of raw data, thereby preserving privacy and minimizing communication overhead. The model leverages quantum advantage in pattern recognition to enhance detection sensitivity, particularly in complex and dynamic IoT network traffic. Experiments on a real-world IoT dataset show that the proposed method delivers anomaly detection accuracy and robustness comparable to centralized approaches, while ensuring data privacy.
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.