Quantum Mixed-State Self-Attention Network
Fu Chen Qinglin Zhao Li Feng Chuangtao Chen Yangbin Lin Jianhong Lin
The rapid advancement of quantum computing has increasingly highlighted its
potential in the realm of machine learning, particularly in the context of
natural language processing (NLP) tasks. Quantum machine learning (QML)
leverages the unique capabilities of quantum computing to offer novel
perspectives and methodologies for complex data processing and pattern
recognition challenges. This paper introduces a novel Quantum Mixed-State
Attention Network (QMSAN), which integrates the principles of quantum computing
with classical machine learning algorithms, especially self-attention networks,
to enhance the efficiency and effectiveness in handling NLP tasks. QMSAN model
employs a quantum attention mechanism based on mixed states, enabling efficient
direct estimation of similarity between queries and keys within the quantum
domain, leading to more effective attention weight acquisition. Additionally,
we propose an innovative quantum positional encoding scheme, implemented
through fixed quantum gates within the quantum circuit, to enhance the model's
accuracy. Experimental validation on various datasets demonstrates that QMSAN
model outperforms existing quantum and classical models in text classification,
achieving significant performance improvements. QMSAN model not only
significantly reduces the number of parameters but also exceeds classical
self-attention networks in performance, showcasing its strong capability in
data representation and information extraction. Furthermore, our study
investigates the model's robustness in different quantum noise environments,
showing that QMSAN possesses commendable robustness to low noise.