Model-independent learning of quantum phases of matter with quantum convolutional neural networks
Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for
gapped quantum phases of matter. Here, we propose a model-independent protocol for …
gapped quantum phases of matter. Here, we propose a model-independent protocol for …
Map** out phase diagrams with generative classifiers
One of the central tasks in many-body physics is the determination of phase diagrams.
However, map** out a phase diagram generally requires a great deal of human intuition …
However, map** out a phase diagram generally requires a great deal of human intuition …
Clustering neural quantum states via diffusion maps
We discuss and demonstrate an unsupervised machine-learning procedure to detect
topological order in quantum many-body systems. Using a restricted Boltzmann machine to …
topological order in quantum many-body systems. Using a restricted Boltzmann machine to …
Unsupervised machine learning for the detection of exotic phases in skyrmion phase diagrams
FA Gómez Albarracín - Physical Review B, 2024 - APS
Undoubtedly, machine learning (ML) techniques are being increasingly applied to a wide
range of situations in the field of condensed matter. Amongst these techniques …
range of situations in the field of condensed matter. Amongst these techniques …
Unsupervised machine learning for the detection of exotic phases in skyrmion phase diagrams
FA Albarracín - arxiv preprint arxiv:2404.10943, 2024 - arxiv.org
Undoubtedly, machine learning techniques are being increasingly applied to a wide range
of situations in the field of condensed matter. Amongst these techniques, unsupervised …
of situations in the field of condensed matter. Amongst these techniques, unsupervised …
Learning symmetry-protected topological order from trapped-ion experiments
Classical machine learning has proven remarkably useful in post-processing quantum data,
yet typical learning algorithms often require prior training to be effective. In this work, we …
yet typical learning algorithms often require prior training to be effective. In this work, we …
Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases
of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating …
of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating …
Realization and characterization of topological phases of matter on a digital quantum computer
Y Liu - 2024 - mediatum.ub.tum.de
The discovery of topological order has revised the understanding of quantum matter and
provided the theoretical foundation for many quantum error–correcting codes. Finding …
provided the theoretical foundation for many quantum error–correcting codes. Finding …
[PDF][PDF] Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases
of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating …
of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating …