Unveiling the microscopic origins of quantum many-body phases dominated by the interplay of spin and charge degrees of freedom constitutes one of the central challenge in modern strongly correlated many-body physics. When holes hop through a background of insulating spins, they displace their positions, which in turn induces effective frustration in the magnetic background. However, the...
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...
Being able to efficiently represent mixed quantum states is essential in order to describe the effects of dissipation, such as those arising in Open Quantum Systems, or in order to represent the noisy outcome of a circuit executed on a present-day Quantum Computer.
The challenges in the description of such objects arise from the exponential growth of the Hilbert space and from the need to...
The application of Machine Learning techniques in many field of theoretical physics has been very successful in the last years, leading to great improvement over existing standard methods.
In this work, we demonstrate how deploying deep generative machine learning models for estimating thermodynamic observables in lattice field theory
is a promising route for addressing many drawbacks...
Quantum scrambling is the process by which quantum information is spread within the degrees of freedom of many-body quantum systems. As such, understanding what are the features of a quantum system that maximise this information spreading has become a recent topic of interest of crucial importance. Graph theory provides a natural mathematical framework to encode the interactions of a quantum...
