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Found 10 papers, 4 papers with code
Quantum Many-Body Physics Calculations with Large Language Models
We evaluate GPT-4's performance in executing the calculation for 15 research papers from the past decade, demonstrating that, with correction of intermediate steps, it can correctly derive the final Hartree-Fock Hamiltonian in 13 cases and makes minor errors in 2 cases.
Materials Expert-Artificial Intelligence for Materials Discovery
The advent of material databases provides an unprecedented opportunity to uncover predictive descriptors for emergent material properties from vast data space.
Machine learning reveals features of spinon Fermi surface
With rapid progress in simulation of strongly interacting quantum Hamiltonians, the challenge in characterizing unknown phases becomes a bottleneck for scientific progress.
Machine learning discovery of new phases in programmable quantum simulator snapshots
Specifically, we apply Hybrid-CCNN to analyze new quantum phases on square lattices with programmable interactions.
The 2021 Quantum Materials Roadmap
In recent years, the notion of Quantum Materials has emerged as a powerful unifying concept across diverse fields of science and engineering, from condensed-matter and cold atom physics to materials science and quantum computing.
Mesoscale and Nanoscale Physics Materials Science Strongly Correlated Electrons Superconductivity Quantum Physics
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states.
Attention-based Quantum Tomography
Here we propose the "Attention-based Quantum Tomography" (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state.
Machine learning out-of-equilibrium phases of matter
We find that this method outperforms conventional metrics (like the entanglement entropy) for identifying MBL phase transitions, revealing a sharper phase boundary and shedding new insight into the topology of the phase diagram.
Disordered Systems and Neural Networks
