首页/论文/2005.11149

arXiv 2005.111492020-05quant-phcs.LGcs.SYeess.SY

On compression rate of quantum autoencoders: Control design, numerical and experimental realization

University of New South Wales（新南威尔士大学）；University of Science and Technology of China（中国科学技术大学）；CAS Center for Excellence in Quantum Information and Quantum Physics（中国科学院量子信息与量子科技创新研究院）；Nanjing University（南京大学）；Griffith University（格里菲斯大学）；Tsinghua University（清华大学）

Hailan Ma, Chang-Jiang Huang, Chunlin Chen, Daoyi Dong, Yuanlong Wang, Re-Bing Wu, Guo-Yong Xiang

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

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. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Quantum autoencoders for efficient compression of quantum data

Classical autoencoders are neural networks that can learn efficient low dimensional representations of data in higher dimensional space. The task of an autoencoder is, given an input $x$, is to map $x$ to a lower dimensional point $y$ such that $x$ can likely be recovered from $y$. The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a particul…

2017-02-10quant-ph

Realization of a quantum autoencoder for lossless compression of quantum data

As a ubiquitous aspect of modern information technology, data compression has a wide range of applications. Therefore, a quantum autoencoder which can compress quantum information into a low-dimensional space is fundamentally important to achieve automatic data compression in the field of quantum information. Such a quantum autoencoder can be implemented through training the parameters of a quantum device using classical optimization algorithms. In this article, we analyze the condition of achieving a perfect quantum autoencoder and theoretically prove that a quantum autoencoder can losslessly…

2020-04-06quant-ph

PALQA：一种使用LSB控制量子比特的新型参数化位置感知有损量子自编码器，用于高效图像压缩

PALQA: A Novel Parameterized Position-Aware Lossy Quantum Autoencoder using LSB Control Qubit for Efficient Image Compression

本文提出PALQA量子自编码器电路，利用LSB控制量子比特和变换系数块状态连接方法实现高效图像压缩，在门数量和PSNR上优于JPEG及现有量子自编码器。

2025-02-04cs.ETquant-ph

Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning

With quantum resources a precious commodity, their efficient use is highly desirable. 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. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the data set allows lossless compression, our autoencoder reduces qutrits to qubits wi…

2019-02-15physics.opticsquant-ph

基于可解释量子自编码器的脑MRI压缩驱动异常检测

Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder

Kingston University（金史密斯大学）；Quantum AI (QAI) Research Group（量子人工智能研究组）；School of Computer Science and Mathematics (CSM)（计算机科学与数学学院）；Faculty of Engineering, Computing and the Environment (ECE)（工程、计算与环境学院）

提出一种量子自编码器（QAE），利用角度编码和变分编码器-解码器架构，通过压缩异常检测脑MRI中的肿瘤区域，在切片级和块级ROC-AUC上分别达到约0.95和0.813，优于经典自编码器和PCA基线。

2026-06-29cs.AIeess.IVquant-ph

基于隐含子群量子自编码器的信息压缩

Information compression via hidden subgroup quantum autoencoders

本研究提出基于隐含子群量子算法的量子自编码器信息压缩方法，可指数级快于经典算法，实验优于经典自编码器，展现量子计算在特定数据压缩上的应用潜力。

2024-08-13quant-ph

更多相关论文收起更多论文

A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning

University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）

Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work evaluates the feasibility of using the D-Wave as a sampler for machine learning. We describe a hybrid system that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave. We evaluate our hybrid autoencoder algorithm using two datasets, the MNIST dataset and MNIST Fashion dataset. We evaluate the quality of this method by using a downstream classification method where the training is based on quantum …

2020-01-31cs.ETcs.LGcs.NE

Noise-Assisted Quantum Autoencoder

Quantum autoencoder is an efficient variational quantum algorithm for quantum data compression. However, previous quantum autoencoders fail to compress and recover high-rank mixed states. In this work, we discuss the fundamental properties and limitations of the standard quantum autoencoder model in more depth, and provide an information-theoretic solution to its recovering fidelity. Based on this understanding, we present a noise-assisted quantum autoencoder algorithm to go beyond the limitations, our model can achieve high recovering fidelity for general input states. Appropriate noise chann…

2021-04-24quant-ph

量子自编码器的神经架构搜索算法

Neural Architecture Search Algorithms for Quantum Autoencoders

Fujitsu Research of America（富士通美国研究机构）；Department of Computer and Information Sciences, University of Delaware（德雷塞尔大学计算机与信息科学系）

本文提出两种 Quantum-NAS 算法，借鉴神经架构搜索自动设计量子电路，并在量子数据去噪、经典数据压缩和纯量子数据压缩任务上取得优于基线的自编码器性能，显著减少人工设计工作量。

2025-09-18cs.LGquant-ph

量子启发的张量网络自编码器用于异常检测：一种MERA方法

Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach

Department of Physics, Sinop University（锡诺普大学物理系）；Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院量子材料与技术研究所）

本文提出基于MERA的自编码器用于碰撞喷流异常检测，探讨多尺度张量网络架构在重建异常检测中的诱导偏差，通过基准比较和训练自由诊断验证局部保持多尺度结构的有效性。

2026-04-08cs.LGhep-phquant-ph

Quantum compression with classically simulatable circuits

University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究院）；Canadian Institute for Advanced Research（加拿大高等研究院）

As we continue to find applications where the currently available noisy devices exhibit an advantage over their classical counterparts, the efficient use of quantum resources is highly desirable. The notion of quantum autoencoders was proposed as a way for the compression of quantum information to reduce resource requirements. Here, we present a strategy to design quantum autoencoders using evolutionary algorithms for transforming quantum information into lower-dimensional representations. We successfully demonstrate the initial applications of the algorithm for compressing different families …

2022-07-06cs.LGcs.NEquant-ph