Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach

• URL: http://arxiv.org/abs/2111.01536v1
• Date: Fri, 29 Oct 2021 23:09:31 GMT
• Title: Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach
• Authors: Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob
• Abstract summary: We show that c-HQMMs are equivalent to a constrained tensor network. This equivalence enables us to provide an efficient learning model for c-HQMMs. The proposed learning approach is evaluated on six real datasets.
• Score: 34.77250498401055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose circular Hidden Quantum Markov Models (c-HQMMs), which can be applied for modeling temporal data in quantum datasets (with classical datasets as a special case). We show that c-HQMMs are equivalent to a constrained tensor network (more precisely, circular Local Purified State with positive-semidefinite decomposition) 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 on multiple datasets as compared to HQMMs, circular HMMs, and HMMs.

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