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Updated: May 17, 2026
09:23
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
Published on: May 30, 2014
Neural Quantum States for Light Nuclei with Chiral Two- and Three-Body Interactions.
Pengsheng Wen1,2, Alexandros Gezerlis3, Jeremy W Holt1,2
1Texas A&M University, Cyclotron Institute, College Station, Texas 77843, USA.
Physical Review Letters
|May 15, 2026
Summary
Neural networks improve quantum Monte Carlo calculations for light nuclei. This approach captures most ground-state energy, significantly outperforming standard methods for nuclear physics simulations.
Area of Science:
- Nuclear Physics
- Computational Physics
- Quantum Mechanics
Background:
- Accurate trial wave functions are crucial for quantum Monte Carlo (QMC) methods in nuclear physics.
- Modeling interparticle correlations and many-body effects, especially three-body interactions, is challenging.
- Existing methods often require significant computational resources and strong intuition.
Purpose of the Study:
- To design neural networks for generating expressive wave function Ansätze for light nuclei.
- To improve the efficiency and accuracy of variational Monte Carlo (VMC) calculations.
- To incorporate many-body effects beyond pairwise correlations.
Main Methods:
- Designing neural networks to model complex correlations in light nuclei.
- Utilizing neural networks within a variational Monte Carlo framework.
- Comparing results with Green's function Monte Carlo (GFMC) for validation.
Main Results:
- The neural network approach for A=3 nuclei captures the majority of ground-state energy at the VMC level.
- Achieved a ground-state energy within 0.45% of GFMC for ^{3}H with a soft chiral interaction.
- Demonstrated a substantial improvement over standard VMC, which showed a 3.7% deviation.
Conclusions:
- Neural networks offer a powerful tool for constructing effective trial wave functions in QMC.
- This approach significantly enhances the accuracy of nuclear structure calculations.
- The method shows great potential for future applications in quantum many-body problems.
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