A new quantum machine learning algorithm: split hidden quantum Markov
model inspired by quantum conditional master equation
- URL: http://arxiv.org/abs/2307.08640v5
- Date: Tue, 16 Jan 2024 15:19:17 GMT
- Title: A new quantum machine learning algorithm: split hidden quantum Markov
model inspired by quantum conditional master equation
- Authors: Xiao-Yu Li, Qin-Sheng Zhu, Yong Hu, Hao Wu, Guo-Wu Yang, Lian-Hui Yu,
Geng Chen
- Abstract summary: We introduce the split HQMM (SHQMM) for implementing the hidden quantum Markov process.
Experimental results suggest our model outperforms previous models in terms of scope of applications and robustness.
- Score: 14.978135681940387
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The Hidden Quantum Markov Model (HQMM) has significant potential for
analyzing time-series data and studying stochastic processes in the quantum
domain as an upgrading option with potential advantages over classical Markov
models. In this paper, we introduced the split HQMM (SHQMM) for implementing
the hidden quantum Markov process, utilizing the conditional master equation
with a fine balance condition to demonstrate the interconnections among the
internal states of the quantum system. The experimental results suggest that
our model outperforms previous models in terms of scope of applications and
robustness. Additionally, we establish a new learning algorithm to solve
parameters in HQMM by relating the quantum conditional master equation to the
HQMM. Finally, our study provides clear evidence that the quantum transport
system can be considered a physical representation of HQMM. The SHQMM with
accompanying algorithms present a novel method to analyze quantum systems and
time series grounded in physical implementation.
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