Efficient and quantum-adaptive machine learning with fermion neural networks
Pei-Lin Zheng, Jiabao Wang, Yi Zhang · Physical Review Applied · 2023
Application of machine learning to quantum data and models has been held back by the lack of adaptability and efficiency in present-day neural networks. This study proposes fermion neural networks for quantum-adaptive machine learning with direct applications to complex quantum systems, and offers in situ analysis without preprocessing or presumption. An efficient optimization comparable to back propagation is established, enabling competitive performance in challenging machine-learning benchmarks. Fermion neural networks' quantum properties, such as entanglement and correlation, also bring various advantages and insights.