During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

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

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

We report new Gaussian boson sampling experiments with pseudo-photon-number-
resolving detection, which register up to 255 photon-click events. We consider partial photon …

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 give a polynomial time classical algorithm for sampling from the output distribution of a
noisy random quantum circuit in the regime of anti-concentration to within inverse …

Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …

The classical simulation of highly entangling quantum dynamics is conjectured to be
generically hard. Thus, recently discovered measurement-induced transitions between …

Despite the development of increasingly capable quantum computers, an experimental
demonstration of a provable algorithmic quantum speedup employing today's non-fault …