Programmable quantum simulators and quantum computers are opening unprecedented
opportunities for exploring and exploiting the properties of highly entangled complex …

The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …

The nonequilibrium physics of many-body quantum systems harbors various unconventional
phenomena. In this Letter, we experimentally investigate one of the most puzzling of these …

Variational quantum algorithms are promising algorithms for achieving quantum advantage
on near-term devices. The quantum hardware is used to implement a variational wave …

We consider the problem of jointly estimating expectation values of many Pauli observables,
a crucial subroutine in variational quantum algorithms. Starting with randomized …

We provide practical and powerful schemes for learning properties of a quantum state using
a small number of measurements. Specifically, we present a randomized measurement …

Advances in isolating, controlling and entangling quantum systems are transforming what
was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and …

The Haar measure plays a vital role in quantum information, but its study often requires a
deep understanding of representation theory, posing a challenge for beginners. This tutorial …