A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation

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.

References in corpus (10)

• Quantum Machine Learning
• Quantum Simulation
• Noisy intermediate-scale quantum (NISQ) algorithms
• Elucidating Reaction Mechanisms on Quantum Computers
• Challenges and Opportunities in Quantum Machine Learning
• Quantum computing for finance: overview and prospects
• Exact dynamics of dissipative electronic systems and quantum transport: Hierarchical equations of motion approach
• Quantum master equation approach to quantum transport through mesoscopic systems
• Optimal stochastic modelling with unitary quantum dynamics
• Memory compression and thermal efficiency of quantum implementations of non-deterministic hidden Markov models