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Supervised Learning with Projected Entangled Pair States [article]

Song Cheng, Lei Wang, Pan Zhang
2020 arXiv   pre-print
In this work, we construct supervised learning models for images using the projected entangled pair states (PEPS), a two-dimensional tensor network having a similar structure prior to natural images.  ...  Our approach first performs a feature map, which transforms the image data to a product state on a grid, then contracts the product state to a PEPS with trainable parameters to predict image labels.  ...  Notice that in physics there is one kind of tensor network with exactly the same geometric structure as the natural images, known as Projected Entangled Pair States (PEPS) 1, 3 . which is composed of  ... 
arXiv:2009.09932v1 fatcat:5ok7mhn5uradnpt4rtdbruqrpq

Entanglement-Based Machine Learning on a Quantum Computer

X.-D. Cai, D. Wu, Z.-E. Su, M.-C. Chen, X.-L. Wang, Li Li, N.-L. Liu, C.-Y. Lu, J.-W. Pan
2015 Physical Review Letters  
implement supervised and unsupervised machine learning.  ...  A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers.  ...  To generate three-and four-photon entanglement resource state, we create two entangled photon pairs.  ... 
doi:10.1103/physrevlett.114.110504 pmid:25839250 fatcat:uhqo4db5efdynlvhead3dv6uhy

Learning Pose-invariant 3D Object Reconstruction from Single-view Images [article]

Bo Peng, Wei Wang, Jing Dong, Tieniu Tan
2020 arXiv   pre-print
Experiments on single-view reconstruction show effectiveness in solving pose entanglement, and the proposed method achieves on-par reconstruction accuracy with state-of-the-art with higher efficiency.  ...  Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data.  ...  Learning with multi-view supervision.  ... 
arXiv:2004.01347v2 fatcat:cxbjpkdtsvbexjam7zquv2ev6a

On Disentangled Representations Learned From Correlated Data [article]

Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer
2021 arXiv   pre-print
We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.  ...  We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement  ...  Weak supervision mitigates learning latent entanglement. We now return to the weakly-supervised method from Section 2 and evaluate its applicability when training data is correlated.  ... 
arXiv:2006.07886v3 fatcat:irkinvphx5drdep6fe2kul2iby

Entanglement assisted training algorithm for supervised quantum classifiers [article]

Soumik Adhikary
2020 arXiv   pre-print
Here, we have harnessed the property of quantum entanglement to build a model that can simultaneously manipulate multiple training samples along with their labels.  ...  We propose a new training algorithm for supervised quantum classifiers.  ...  We show that this allows us to encode pairs of training samples, with opposite labels, into entangled states. A Bell test on these states leads us to our cost function.  ... 
arXiv:2006.13302v2 fatcat:3frpkw7kcnfarks6cqyntvyrm4

Quantum image classifier with single photons [article]

Kunkun Wang, Lei Xiao, Wei Yi, Shi-Ju Ran, Peng Xue
2020 arXiv   pre-print
Adopting a tensor-network-based machine learning algorithm with an entanglement-guided optimization, we achieve an efficient representation of the quantum feature space using matrix product states.  ...  Machine learning, with promising applications in quantum computation, has been introduced to a variety of quantum mechanical platforms, where its interplay with quantum physics offers exciting prospects  ...  The gate operations in the single-photon interferometry network are optimized through supervised learning on classical computers, and results of the classification are read out through projective measurements  ... 
arXiv:2003.08551v1 fatcat:x7toei4izjcqvd6shkheksoxru

Machine learning spatial geometry from entanglement features

Yi-Zhuang You, Zhao Yang, Xiao-Liang Qi
2018 Physical review B  
Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement  ...  We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state.  ...  Acknowledgments The authors would like to acknowledge the helpful discussions with Pan Zhang, Yanran Li, and Roger Melko.  ... 
doi:10.1103/physrevb.97.045153 fatcat:54dkvdov6bffbl642ovz63bs5i

STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning [article]

Prakamya Mishra
2020 arXiv   pre-print
Unlike existing work, we have used text along with speech for auditory representation learning to capture semantical and syntactical information along with the acoustic and temporal information.  ...  In this paper, we present a novel multi-modal deep neural network architecture that uses speech and text entanglement for learning phonetically sound spoken-word representations.  ...  Both f C & f W , are then used to entangle speech and text-based contextual information with the target spoken-word by generating new speech and text entangled bidirectional hidden state representations  ... 
arXiv:2011.11387v1 fatcat:b4zz3rvitrhfpornz7uxnuje3y

Deep learning of topological phase transitions from entanglement aspects: An unsupervised way [article]

Yuan-Hong Tsai, Kuo-Feng Chiu, Yong-Cheng Lai, Kuan-Jung Su, Tzu-Pei Yang, Tsung-Pao Cheng, Guang-Yu Huang, Ming-Chiang Chung
2021 arXiv   pre-print
B 102, 054512 (2020)] and further on the Su-Schrieffer-Heeger model, with an emphasis on using the quantum entanglement-based quantities as the input features.  ...  We conclude with a few remarks about its potential, limitations, and explainabilities.  ...  network via supervised learning approach.  ... 
arXiv:2105.03870v3 fatcat:jbazxihcirh2jlac4lonvpypyu

Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets [article]

Kunal Sharma, M. Cerezo, Zoë Holmes, Lukasz Cincio, Andrew Sornborger, Patrick J. Coles
2020 arXiv   pre-print
The No-Free-Lunch (NFL) theorem is a celebrated result in learning theory that limits one's ability to learn a function with a training data set.  ...  Our work establishes that entanglement is a commodity in quantum machine learning.  ...  (b) In quantum supervised learning, the goal is to learn a ddimensional unitary process with t quantum states serving as training data.  ... 
arXiv:2007.04900v1 fatcat:7clzaz2g6vgjtl4bn6qnfjquom

Effective routing design for remote entanglement generation on quantum networks

Changhao Li, Tianyi Li, Yi-Xiang Liu, Paola Cappellaro
2021 npj Quantum Information  
Efficient entanglement generation on quantum networks with relatively limited resources such as quantum memories is essential to fully realize the network's capabilities, the solution to which calls for  ...  In this study we propose an effective routing scheme to enable automatic responses for multiple requests of entanglement generation between source-terminal stations on a quantum lattice network with finite  ...  Different Bell states or states with different entanglement entropy might serve as identities (ID) for entangled pairs for further usages in the network layer 8 .  ... 
doi:10.1038/s41534-020-00344-4 fatcat:yemmaukj4banpglfabqgvmaun4

Multi-View Image-to-Image Translation Supervised by 3D Pose [article]

Idit Diamant, Oranit Dror, Hai Victor Habi, Arnon Netzer
2021 arXiv   pre-print
The joint learning is imposed by constraints on the shared 3D human pose in order to encourage the 2D pose projections in all views to be consistent.  ...  The goal is to synthesize photo-realistic multi-view images with pose-consistency across all views.  ...  The aforementioned pose-guided approaches learn their translation in a supervised manner by samples of pose-corresponding image pairs [21, 27, 30, 12] .  ... 
arXiv:2104.05779v1 fatcat:how22ieatbco7huk4m263tzo3y

A New Quantum Approach to Binary Classification [article]

Dr. G. Arun Sampaul Thomas, Krishna Sai Mangalarapu, Munawar Ali Md, Vamsi Krishna Talakokkula
2021 arXiv   pre-print
Machine Learning classification models learn the relation between input as features and output as a class in order to predict the class for the new given input.  ...  In recent years, researchers have been trying to investigate whether the QM can help to improve the classical machine learning algorithms.  ...  Supervised learning includes using an algorithm to learn the mapping function, y=f(x) from the input to the output based on previous examples of input-output pairs.  ... 
arXiv:2106.15572v1 fatcat:cxzhtenewnctrfaytopbmd6gka

Entanglement quantification from collective measurements processed by machine learning [article]

Jan Roik, Karol Bartkiewicz, Antonín Černoch, Karel Lemr
2022 arXiv   pre-print
For the purpose of our research, we consider general two-qubit states and their negativity as entanglement quantifier.  ...  instances of the investigated state).  ...  technique of supervised learning.  ... 
arXiv:2203.01607v1 fatcat:g6m3qpdwcjfpbdige2437lc4bu

Quantum Hierarchical Clustering Algorithm Based on the Nearest Cluster Centroids Distance

Fengbo Kong, Hong Lai, Hailing Xiong
2017 International Journal of Machine Learning and Computing  
For quantum entanglement, the distance between two data points is calculated through adding an auxiliary particle to construct the entangled state.  ...  Index Terms-Large data, hierarchical clustering, qubit, entangled states. Fengbo Kong is currently a graduate student at Beijing University of Posts and Telecommunications in 2015.  ...  [25] implemented an experiment, based on entangled quantum machine learning.  ... 
doi:10.18178/ijmlc.2017.7.5.628 fatcat:tvztbrggljhatnqoi7mejnbtre
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