Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …

The “sign problem”(SP) is a fundamental limitation to simulations of strongly correlated
matter. It is often argued that the SP is not intrinsic to the physics of particular Hamiltonians …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

The transistor celebrated its 75th birthday in 2022. The continued scaling of the transistor
defined by Moore's law continues, albeit at a slower pace. Meanwhile, computing demands …

This work presents a novel realization approach to quantum Boltzmann machines (QBMs).
The preparation of the required Gibbs states, as well as the evaluation of the loss function's …

We investigate how the computational difficulty of contracting tensor networks depends on
the sign structure of the tensor entries. Using results from computational complexity, we …

Sign problem in quantum Monte Carlo (QMC) simulation appears to be an extremely hard
yet interesting problem. In this article, we present a pedagogical overview on the origin of …

Variational algorithms require architectures that naturally constrain the optimisation space to
run efficiently. In geometric quantum machine learning, one achieves this by encoding group …

We present a simulation strategy for the real-time dynamics of quantum fields, inspired by
reinforcement learning. It builds on the complex Langevin approach, which it amends with …

The mystery of the infamous sign problem in quantum Monte Carlo simulations mightily
restricts applications of the method in fermionic and frustrated systems. A recent work …