# Learning Potentials of Quantum Systems using Deep Neural Networks

arXiv: https://arxiv.org/abs/2006.13297 · HTML: https://mlsanity.com/paper/2006.13297/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Tensor networks for interpretable and efficient quantum-inspired machine learning](https://mlsanity.com/paper/2311.11258/) (2023)
- [Scalable Neural Network Kernels](https://mlsanity.com/paper/2310.13225/) (2023)
- [Deep Neural Network Based Differential Equation Solver for HIV Enzyme Kinetics](https://mlsanity.com/paper/2102.08471/) (2021)
- [Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling](https://mlsanity.com/paper/2106.03126/) (2021)
- [Learning Full Configuration Interaction Electron Correlations with Deep Learning](https://mlsanity.com/paper/2106.08138/) (2021)
- [Generating extreme quantum scattering in graphene with machine learning](https://mlsanity.com/paper/2212.06929/) (2022)

## Abstract (arXiv)

Attempts to apply Neural Networks (NN) to a wide range of research problems have been ubiquitous and plentiful in recent literature. Particularly, the use of deep NNs for understanding complex physical and chemical phenomena has opened a new niche of science where the analysis tools from Machine Learning (ML) are combined with the computational concepts of the natural sciences. Reports from this unification of ML have presented evidence that NNs can learn classical Hamiltonian mechanics. This application of NNs to classical physics and its results motivate the following question: Can NNs be endowed with inductive biases through observation as means to provide insights into quantum phenomena? In this work, this question is addressed by investigating possible approximations for reconstructing the Hamiltonian of a quantum system in an unsupervised manner by using only limited information obtained from the system's probability distribution.