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Updated: Feb 24, 2026
06:42
Generation and Coherent Control of Pulsed Quantum Frequency Combs
Published on: June 8, 2018
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Representational power of selected neural network quantum states in second quantization.
Zhendong Li1, Tong Zhao2, Bohan Zhang1
1Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
The Journal of Chemical Physics
|February 23, 2026
Summary
Neuron product states (NPS) offer a new way to represent complex fermionic quantum states. These states, built from simple nonlocal correlators, can approximate any wavefunction, advancing quantum many-body problem solutions.
Area of Science:
- Computational Physics
- Quantum Mechanics
- Machine Learning
Background:
- Neural network quantum states are emerging tools for quantum many-body problems.
- Understanding their capabilities and limitations, especially for fermions, is crucial.
Purpose of the Study:
- Generalize the restricted Boltzmann machine Ansatz for fermionic states.
- Introduce and analyze Neuron Product States (NPS) for quantum many-body problems.
Main Methods:
- Generalizing restricted Boltzmann machine Ansatz to Neuron Product States (NPS).
- Proving universal approximation capabilities of NPS, feedforward neural networks, and neural network backflow in second quantization.
Main Results:
- NPS utilize simple nonlocal correlators, differing from correlator product states.
- NPS can approximate any wavefunction under mild conditions.
- Established universal approximation for neural network representations in second quantization.
Conclusions:
- NPS provide a novel and powerful framework for representing fermionic quantum states.
- The study deepens the understanding of neural network capabilities in solving quantum many-body problems.
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