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Supervised Learning with Projected Entangled Pair States
[article]
2020
arXiv
pre-print
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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
2015
Physical Review Letters
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2014 pre-print arXiv:1409.7770v2 fatcat:mdvqjb62xja7vkovuz4bdusnjaOther Versions
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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2018
Physical review B
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2021
npj Quantum Information
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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]
2021
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2017
International Journal of Machine Learning and Computing
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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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