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arXiv · 2111.01536

Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach

Mohammad Ali Javidian·Vaneet Aggarwal·Zubin Jacob
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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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Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob. 2021-10-29. Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach. https://arxiv.org/abs/2111.01536

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