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 …

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 …

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 …

We analyze the accuracy and sample complexity of variational Monte Carlo approaches to
simulate the dynamics of many-body quantum systems classically. By systematically …

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 …

Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

Recurrent neural networks (RNNs), originally developed for natural language processing,
hold great promise for accurately describing strongly correlated quantum many-body …

Finding ground states of quantum many-body systems is known to be hard for both classical
and quantum computers. As a result, when Nature cools a quantum system in a low …

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 …

Large language models, like transformers, have recently demonstrated immense powers in
text and image generation. This success is driven by the ability to capture long-range …