The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

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

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

Interacting many-electron problems pose some of the greatest computational challenges in
science, with essential applications across many fields. The solutions to these problems will …

Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …

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 …

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 present an efficient machine-learning (ML) algorithm for predicting any unknown
quantum process E over n qubits. For a wide range of distributions D on arbitrary n-qubit …

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 …