Machine learning quantum phases of matter beyond the fermion sign problem
By Peter Broecker, Juan Felipe Carrasquilla, Roger G. Melko, Simon Trebst. Scientific Reports, 2016
310 citations recorded by the source corpus.
State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated way. Here we demonstrate that convolutional neural networks (CNN) can be optimized for quantum many-fermion systems such that they correctly identify and locate quantum phase transitions in such systems. Using auxiliary-field quantum Monte Carlo (QMC) simulations to sample the many-fermion system, we show that the Green’s function holds sufficient information to allow for the distinction of different fermionic phases via a CNN. We demonstrate that this QMC + machine learning approach works even for systems exhibiting a severe fermion sign problem where conventional approaches to extract information from the Green’s function, e.g. in the form of equal-time correlation functions, fail.
Subfields: Atomic and Molecular Physics, and Optics, Condensed Matter Physics, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/c47a860451768d329e5d7e492ef209614f5ef7c8
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In Atomic and Molecular Physics, and Optics
In Condensed Matter Physics
In Materials Chemistry
Citations in the corpus
- Quantum Entanglement in Neural Network States (2017, background): “Exciting progress has been made in identifying quantum phases and transitions among them (either conventional symmetry-broken [33, 35, 37, 38] or topological phases [32]), modeling thermodynamic observables [36], constructing decoders for topological codes [39], accelerating Monte Carlo simulations…”
- Systems Biology of Cancer Metastasis (2019, methodology): “…in deep learning methods have been applied in the analysis of complex systems of interacting components seen in various physical systems(Broecker et al., 2017; Schindler et al., 2017), and similar methods may be ripe for the analysis of the dynamics of cell populations in metastasis,…”
- Machine Learning Phases of Strongly Correlated Fermions (2016, unknown): “The latter can call into question a recent suggestion that the sign problem can be circumvented using neural networks by essentially ignoring the sign in training or classifications [24].”
- Quantum machine learning (2016, unknown): “Neural networks have recently been deployed to study two central problems in condensed matter, namely phase of matter detection [80, 81] and ground state search [82].”