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

With the rapid development of quantum technologies, a pressing need has emerged for a
wide array of tools for the certification and characterization of quantum devices. Such tools …

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

Predicting the properties of complex, large-scale quantum systems is essential for
developing quantum technologies. We present an efficient method for constructing an …

We propose a method for detecting bipartite entanglement in a many-body mixed state
based on estimating moments of the partially transposed density matrix. The estimates are …

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 …

We study the power of quantum memory for learning properties of quantum systems and
dynamics, which is of great importance in physics and chemistry. Many state-of-the-art …

We propose a simple scheme to reduce readout errors in experiments on quantum systems
with finite number of measurement outcomes. Our method relies on performing classical …

The operator entanglement (OE) is a key quantifier of the complexity of a reduced density
matrix. In out-of-equilibrium situations, eg, after a quantum quench of a product state, it is …

Efficiently estimating properties of large and strongly coupled quantum systems is a central
focus in many-body physics and quantum information theory. While quantum computers …