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 …

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 …

Monitored quantum dynamics—unitary evolution interspersed with measurements—has
recently emerged as a rich domain for phase structure in quantum many-body systems away …

Advances in quantum technology require scalable techniques to efficiently extract
information from a quantum system. Traditional tomography is limited to a handful of qubits …

We generalize the classical shadow tomography scheme to a broad class of finite-depth or
finite-time local unitary ensembles, known as locally scrambled quantum dynamics, where …

The classical shadows protocol, recently introduced by Huang, Kueng, and Preskill [Nat.
Phys. 16, 1050 (2020)], is a quantum-classical protocol to estimate properties of an unknown …

Finding the ground state of a quantum many-body system is a fundamental problem in
quantum physics. In this work, we give a classical machine learning (ML) algorithm for …