Phase transitions in a non-perturbative regime can be studied by ab initio Lattice Field
Theory methods. The status and future research directions for LFT investigations of Quantum …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

We extend the concept of transfer learning, widely applied in modern machine learning
algorithms, to the emerging context of hybrid neural networks composed of classical and …

Parametrized quantum circuits (PQCs) have been broadly used as a hybrid quantum-
classical machine learning scheme to accomplish generative tasks. However, whether …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Machine learning, one of today's most rapidly growing interdisciplinary fields, promises an
unprecedented perspective for solving intricate quantum many-body problems …

We develop a machine learning method to construct accurate ground-state wave functions
of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A …

The Landau description of phase transitions relies on the identification of a local order
parameter that indicates the onset of a symmetry-breaking phase. In contrast, topological …

We apply unsupervised machine learning techniques, mainly principal component analysis
(PCA), to compare and contrast the phase behavior and phase transitions in several …