Implementation and Learning of Quantum Hidden Markov Models
Authors: Vanio Markov, Vladimir Rastunkov, Amol Deshmukh, Daniel Fry, Charlee Stefanski
Abstract: Hidden Markov models are a powerful tool for learning and describing sequential data. In this work, we focus on understanding the advantages of using quantum hidden Markov models over classical counterparts. We propose a practical, hardware efficient quantum circuit ansatz, as well as a training algorithm. We compare results from executions of these dynamic circuits using quantum simulators with results from IBM quantum hardware.
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