# Finding Quantum Critical Points with Neural-Network Quantum States

arXiv: https://arxiv.org/abs/2002.02618 · HTML: https://mlsanity.com/paper/2002.02618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States](https://mlsanity.com/paper/2609.23334/) (2026)

## Abstract (arXiv)

Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body systems are notoriously hard to study because the dimension of their Hilbert space increases exponentially with their size. Recently, machine learning tools known as neural-network quantum states have been shown to effectively and efficiently simulate quantum many-body systems. We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states, analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning. We validate the approach and evaluate its efficiency and effectiveness in comparison with other traditional approaches.