A leap among quantum computing and quantum neural networks: A survey
In recent years, Quantum Computing witnessed massive improvements in terms of available
resources and algorithms development. The ability to harness quantum phenomena to solve …
resources and algorithms development. The ability to harness quantum phenomena to solve …
Empowering deep neural quantum states through efficient optimization
Computing the ground state of interacting quantum matter is a long-standing challenge,
especially for complex two-dimensional systems. Recent developments have highlighted the …
especially for complex two-dimensional systems. Recent developments have highlighted the …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
NetKet 3: Machine learning toolbox for many-body quantum systems
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum
physics. NetKet is built around neural-network quantum states and provides efficient …
physics. NetKet is built around neural-network quantum states and provides efficient …
A simple linear algebra identity to optimize large-scale neural network quantum states
Neural-network architectures have been increasingly used to represent quantum many-body
wave functions. These networks require a large number of variational parameters and are …
wave functions. These networks require a large number of variational parameters and are …
Variational benchmarks for quantum many-body problems
The continued development of computational approaches to many-body ground-state
problems in physics and chemistry calls for a consistent way to assess its overall progress …
problems in physics and chemistry calls for a consistent way to assess its overall progress …
Dirac-type nodal spin liquid revealed by refined quantum many-body solver using neural-network wave function, correlation ratio, and level spectroscopy
Pursuing fractionalized particles that do not bear properties of conventional measurable
objects, exemplified by bare particles in the vacuum such as electrons and elementary …
objects, exemplified by bare particles in the vacuum such as electrons and elementary …
Neural-network quantum states for many-body physics
M Medvidović, JR Moreno - arXiv preprint arXiv:2402.11014, 2024 - arxiv.org
Variational quantum calculations have borrowed many tools and algorithms from the
machine learning community in the recent years. Leveraging great expressive power and …
machine learning community in the recent years. Leveraging great expressive power and …
High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks
We show that neural quantum states based on very deep (4–16-layered) neural networks
can outperform state-of-the-art variational approaches on highly frustrated quantum …
can outperform state-of-the-art variational approaches on highly frustrated quantum …
Optimizing design choices for neural quantum states
Neural quantum states are a new family of variational Ansätze for quantum-many body wave
functions with advantageous properties in the notoriously challenging case of two spatial …
functions with advantageous properties in the notoriously challenging case of two spatial …