For quantum computers to successfully solve real-world problems, it is necessary to tackle
the challenge of noise: the errors that occur in elementary physical components due to …

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

Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …

Quantum random sampling is the leading proposal for demonstrating a computational
advantage of quantum computers over classical computers. Recently the first large-scale …

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 …

The rapid pace of development in quantum computing technology has sparked a
proliferation of benchmarks to assess the performance of quantum computing hardware and …

A large ongoing research effort focuses on variational quantum algorithms (VQAs),
representing leading candidates to achieve computational speed-ups on current quantum …

Model bias is an inherent limitation of the current dominant approach to optimal quantum
control, which relies on a system simulation for optimization of control policies. To overcome …

Randomized benchmarking refers to a collection of protocols that in the past decade have
become central methods for characterizing quantum gates. These protocols aim at efficiently …

In this perspective we discuss verification of quantum devices in the context of specific
examples formulated as proposed experiments. Our first example is verification of analog …