Language models for quantum simulation
A key challenge in the effort to simulate today's quantum computing devices is the ability to
learn and encode the complex correlations that occur between qubits. Emerging …
learn and encode the complex correlations that occur between qubits. Emerging …
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 …
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 …
Neural-network quantum states for many-body physics
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 …
Highly resolved spectral functions of two-dimensional systems with neural quantum states
Spectral functions are central to link experimental probes to theoretical models in
condensed matter physics. However, performing exact numerical calculations for interacting …
condensed matter physics. However, performing exact numerical calculations for interacting …
Wave-Function Network Description and Kolmogorov Complexity of Quantum Many-Body Systems
Programmable quantum devices are now able to probe wave functions at unprecedented
levels. This is based on the ability to project the many-body state of atom and qubit arrays …
levels. This is based on the ability to project the many-body state of atom and qubit arrays …
Neural network approach to quasiparticle dispersions in doped antiferromagnets
Numerically simulating large, spinful, fermionic systems is of great interest in condensed
matter physics. However, the exponential growth of the Hilbert space dimension with system …
matter physics. However, the exponential growth of the Hilbert space dimension with system …
Autoregressive neural quantum states of Fermi Hubbard models
Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum
Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural …
Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural …
Neural network quantum states for the interacting Hofstadter model with higher local occupations and long-range interactions
Due to their immense representative power, neural network quantum states (NQS) have
gained significant interest in current research. In recent advances in the field of NQS, it has …
gained significant interest in current research. In recent advances in the field of NQS, it has …