scholarly journals Experimental realization of a quantum image classifier via tensor-network-based machine learning

2021 ◽  
Author(s):  
Peng Xue ◽  
Kun Wang ◽  
Lei Xiao ◽  
Wei Yi ◽  
Shi-Ju Ran
2020 ◽  
Vol 101 (7) ◽  
Author(s):  
Zheng-Zhi Sun ◽  
Cheng Peng ◽  
Ding Liu ◽  
Shi-Ju Ran ◽  
Gang Su

Author(s):  
Ian Convy ◽  
William Huggins ◽  
Haoran Liao ◽  
K Birgitta Whaley

Abstract Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that the success of a given tensor network ansatz depends in part on how well it can reproduce the underlying entanglement structure of the target state, with different network designs favoring different scaling patterns. We demonstrate here how a related correlation analysis can be applied to tensor network machine learning, and explore whether classical data possess correlation scaling patterns similar to those found in quantum states which might indicate the best network to use for a given dataset. We utilize mutual information as measure of correlations in classical data, and show that it can serve as a lower-bound on the entanglement needed for a probabilistic tensor network classifier. We then develop a logistic regression algorithm to estimate the mutual information between bipartitions of data features, and verify its accuracy on a set of Gaussian distributions designed to mimic different correlation patterns. Using this algorithm, we characterize the scaling patterns in the MNIST and Tiny Images datasets, and find clear evidence of boundary-law scaling in the latter. This quantum-inspired classical analysis offers insight into the design of tensor networks which are best suited for specific learning tasks.


2020 ◽  
Vol 102 (6) ◽  
Author(s):  
Chu Guo ◽  
Kavan Modi ◽  
Dario Poletti

2019 ◽  
Vol 21 (7) ◽  
pp. 073059 ◽  
Author(s):  
Ding Liu ◽  
Shi-Ju Ran ◽  
Peter Wittek ◽  
Cheng Peng ◽  
Raul Blázquez García ◽  
...  

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Timo Felser ◽  
Marco Trenti ◽  
Lorenzo Sestini ◽  
Alessio Gianelle ◽  
Davide Zuliani ◽  
...  

AbstractTensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big data problem in high-energy physics: the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from proton–proton collisions in the LHCb experiment, and how to interpret the classification results. We exploit the Tensor Network approach to select important features and adapt the network geometry based on information acquired in the learning process. Finally, we show how to adapt the tree tensor network to achieve optimal precision or fast response in time without the need of repeating the learning process. These results pave the way to the implementation of high-frequency real-time applications, a key ingredient needed among others for current and future LHCb event classification able to trigger events at the tens of MHz scale.


2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Shi-Ju Ran ◽  
Zheng-Zhi Sun ◽  
Shao-Ming Fei ◽  
Gang Su ◽  
Maciej Lewenstein

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