Neural Quantum States for Time Evolution - Challenges and Opportunities

Contribution List

1. Variational dynamics of continuous-variable quantum rotor models

Matija Medvidović

18/11/2024, 14:00

Time-dependent variational Monte Carlo (t-VMC) has emerged as a powerful method of simulating real-time dynamics of correlated quantum systems in the recent years. With wider adoption of variational states based on neural networks, these methods have started to reach experimentally relevant time scales. However, despite rapid progress and growing interest, the t-VMC method is still relatively ...

2. Solving quantum and classical dissipative dynamics with artificial neural networks

Marti Gärttner

18/11/2024, 14:45

We develop a variational approach to simulating the dynamics of open quantum and classical many-body systems using artificial neural networks. The parameters of a compressed representation of a probability distribution are adapted dynamically according to the Lindblad master equation or Fokker Planck equation, respectively, by employing a time-dependent variational principle. We illustrate our...

3. Reinforcement learning for quantum error correction

Marín Bukov

18/11/2024, 16:15

TBA

8. Diving beyond t-VMC with state compression and tailored integration schemes

Filippo Vicentini

18/11/2024, 17:00

TBA

5. Fermions in Motion: A New Approach to Quantum Dynamics

Jannes Nys

19/11/2024, 08:45

Describing the real-time evolution of many-electron quantum systems is crucial for understanding the dynamical properties of condensed matter, molecular systems in quantum chemistry, and the behaviors of complex materials. However, the real-time evolution of non-equilibrium quantum electronic systems poses a significant challenge for theoretical and computational approaches. This work...

6. Real-time quantum dynamics of thermal states with neural thermofields

Zakari Denis

19/11/2024, 09:30

Solving the time-dependent quantum many-body Schrödinger equation is a challenging task, especially for states at a finite temperature, where the environment affects the dynamics. Most existing approximating methods are designed to represent static thermal density matrices, 1D systems, and/or zero-temperature states. In this work, we propose a method to study the real-time dynamics of thermal...

7. Dynamics of quantum matter with classical networks and neural quantum states

Markus Heyl

19/11/2024, 11:00

Neural quantum states have emerged as a novel promising numerical
method to solve the quantum many-body problem both in and out of equilibrium.
In this talk I will discuss recent developments and challenges. I will also
highlight potential ways to solve for some of these challenges.

9. Solving the (time-dependent) Schrödinger equation

Annabelle Bohrdt

19/11/2024, 14:00

Simulating the time evolution of interacting quantum systems is in general a very hard problem. Quantum simulation experiments, such as cold atoms in optical lattices, are naturally well suited to study closed system dynamics. A bottleneck for these setups are slow data taking rates, which enable experimentalists typically only to get a limited number of projective measurements at a limited...

10. Quantum-gas microscopy of Hubbard systems

Johannes Zeiher

19/11/2024, 14:45

Neutral atoms trapped in optical lattices are a versatile platform to study many-body physics in and out of equilibrium.
Quantum gas microscopes provide an excellent toolbox to prepare, control and detect such systems at the level of individual atoms.
First, I will present our recent work on realizing long-range interacting Ising and Hubbard models for Rubidium atoms in optical lattices....

4. Positive Operator Valued Measures Neural Networks for simulation of light-matter coupled systems

Gianluca Lagnese

19/11/2024, 20:00

TBA

11. TBA

Jonas Rigo

20/11/2024, 08:45

TBA

12. Neural network simulation of a spin glass dynamics transition

Juan Carrasquilla

20/11/2024, 09:30

I will explain a recent experiment about the simulation of nonequilibrium dynamics of a magnetic spin system quenched through a quantum glass phase transition and our attempt at using neural networks to simulate the dynamics seen in the experiment.