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Generative tensor network classification model for supervised machine learning
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DOI：10.1103/PhysRevB.101.075135.png)
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摘要
En 中文
The tensor network (TN) has recently triggered extensive interests in developing machine learning models defined in quantum Hilbert space. Here, we propose a generative TN classification (GTNC) model for supervised machine learning. The strategy is first to map the classical data onto the states in a many-body Hilbert space and then to capture these states with tensor network schemes. We adopt the TN in the form of matrix product states as an example to implement GTNC where the testing images are classified by comparing the fidelities between different states. Our results show that GTNC has a very impressive performance on benchmark datasets in comparison to several well-known machine learning models. The advantage of GTNC is reflected from the facts that the samples are naturally clustering in the many-body Hilbert space, and it relies much less on hyperparameters. These characters make GTNC an adaptive and universal quantum-inspired method, which would have important applications in quantum computation and quantum information.
Keyword:
MATRIX PRODUCT STATES
RENORMALIZATION-GROUP
QUANTUM
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期刊
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3.7
论文数:
15.4W
被引数:
41.0W
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分享收藏分享收藏Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems量子自旋系统的矩阵乘积态，投影纠缠态和变分重归一化组方法.png)
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