Search Results - "Quantum Autoencoder"

Anomaly detection in high-energy physics using a quantum autoencoder by Ngairangbam, Vishal S., Spannowsky, Michael, Takeuchi, Michihisa

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Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning by Pepper, Alex, Tischler, Nora, Pryde, Geoff J.

“… Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space…”
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Hybrid classical-quantum autoencoder for anomaly detection by Sakhnenko, Alona, O’Meara, Corey, Ghosh, Kumar J. B., Mendl, Christian B., Cortiana, Giorgio, Bernabé-Moreno, Juan

“…We propose a hybrid classical-quantum autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE…”
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Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder by Zhang, Hui, Wan, Lingxiao, Haug, Tobias, Mok, Wai-Keong, Paesani, Stefano, Shi, Yuzhi, Cai, Hong, Chin, Lip Ket, Karim, Muhammad Faeyz, Xiao, Limin, Luo, Xianshu, Gao, Feng, Dong, Bin, Assad, Syed, Kim, M. S., Laing, Anthony, Kwek, Leong Chuan, Liu, Ai Qun

“…Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems…”
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Clustering and enhanced classification using a hybrid quantum autoencoder by Srikumar, Maiyuren, Hill, Charles D, Hollenberg, Lloyd C L

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Efficient Data Loading with Quantum Autoencoder by Wu, Siang-Ruei, Li, Chun-Tse, Cheng, Hao-Chung

“… In this work, we propose an efficient quantum autoencoder architecture that can construct a quantum state approximating the unknown classical distribution with high precision and with only linear circuit depth…”
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Quantum Autoencoder for Enhanced Fraud Detection in Imbalanced Credit Card Dataset by Huot, Chansreynich, Heng, Sovanmonynuth, Kim, Tae-Kyung, Han, Youngsun

“… In response to these challenges, we propose a novel detection model utilizing Quantum AutoEncoders-based Fraud Detection (QAE-FD…”
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Quantum autoencoder implementation of high-dimensional steganographic encoding for arbitrary quantum states by Hao, Chaolong, Ma, Quangong, Chen, Yaqi, Zhang, Hao, Qu, Dan

“… We progressively explore Quantum Autoencoder (QAE) structures through three stages: starting from single-state scenarios without perturbation, advancing to perturbed conditions, and finally extending to multi-state tasks…”
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Unsupervised beyond-standard-model event discovery at the LHC with a novel quantum autoencoder by Duffy, Callum, Hassanshahi, Mohammad, Jastrzebski, Marcin, Malik, Sarah

“… We introduce a novel quantum autoencoder circuit ansatz that is specifically designed for this task and demonstrates superior performance compared to previous approaches…”
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Quantum autoencoders with enhanced data encoding by Bravo-Prieto, Carlos

“…We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity…”
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Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder by Hui Zhang, Lingxiao Wan, Tobias Haug, Wai-Keong Mok, Stefano Paesani, Yuzhi Shi, Hong Cai, Lip Ket Chin, Muhammad Faeyz Karim, Limin Xiao, Xianshu Luo, Feng Gao, Bin Dong, Syed Assad, M. S. Kim, Anthony Laing, Leong Chuan Kwek, Ai Qun Liu

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Anomaly Detection Based on Quantum Autoencoder by Li, Siqi, Zhang, Zikun, Zhu, Xun, Ou, Yanni, Xu, Kun

“…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…”
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On compression rate of quantum autoencoders: Control design, numerical and experimental realization by Ma, Hailan, Huang, Chang-Jiang, Chen, Chunlin, Dong, Daoyi, Wang, Yuanlong, Wu, Re-Bing, Xiang, Guo-Yong

“…Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information…”
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Noise-Assisted Quantum Autoencoder by Cao, Chenfeng, Wang, Xin

“…Quantum autoencoder is an efficient variational quantum algorithm for quantum data compression…”
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Optimized Quantum Autoencoder by Huang, Yibin, Yang, Muchun, Zhou, D L

“…Quantum autoencoder (QAE) compresses a bipartite quantum state into its subsystem by a self-checking mechanism…”
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Quantum deep learning-based anomaly detection for enhanced network security by Hdaib, Moe, Rajasegarar, Sutharshan, Pan, Lei

“…Identifying and mitigating aberrant activities within the network traffic is important to prevent adverse consequences caused by cyber security incidents,…”
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Hybrid Classical-Quantum Autoencoder for Anomaly Detection by Sakhnenko, Alona, O'Meara, Corey, Ghosh, Kumar J B, Mendl, Christian B, Cortiana, Giorgio, Bernabé-Moreno, Juan

“…We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE…”
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Quantum deep learning-enhanced ethereum blockchain for cloud security: intrusion detection, fraud prevention, and secure data migration by Nagarjun, A. Venkata, Rajkumar, Sujatha

“…Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern…”
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Achieving High-Efficiency Error-Correcting Transmission of Qutrits with On-Chip Quantum Autoencoder by Wang, Denghui, Ma, Haoran, Chen, Donghui, Ye, Liao, Ruan, Fanjie, Wang, Yuehai, Yang, Jianyi

“…We designed a programmable quantum autoencoder on a silicon photonic chip with nearly no compression loss, which enabled us to propose and implement a new highly efficient error-correcting…”
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Quantum neural network autoencoder and classifier applied to an industrial case study by Mangini, Stefano, Marruzzo, Alessia, Piantanida, Marco, Gerace, Dario, Bajoni, Daniele, Macchiavello, Chiara

“… In this work, we propose a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e…”
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