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• W3208781605 abstract "This paper proposes circular Hidden Quantum Markov Models (c-HQMMs), which can be applied for modeling temporal data. We show that c-HQMMs are equivalent to a tensor network (more precisely, circular Local Purified State) model. This equivalence enables us to provide an efficient learning model for c-HQMMs. The proposed learning approach is evaluated on six real datasets and demonstrates the advantage of c-HQMMs as compared to HQMMs and HMMs." @default.
• W3208781605 created "2021-11-08" @default.
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• W3208781605 date "2023-01-01" @default.
• W3208781605 modified "2023-10-03" @default.
• W3208781605 title "Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach" @default.
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• W3208781605 doi "https://doi.org/10.1109/tqe.2023.3319254" @default.
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