Quantum technology promises to revolutionize how we learn about the physical world. An
experiment that processes quantum data with a quantum computer could have substantial …

One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

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 …

Generalization bounds are a critical tool to assess the training data requirements of
Quantum Machine Learning (QML). Recent work has established guarantees for in …

We prove that random quantum circuits on any geometry, including a 1D line, can form
approximate unitary designs over $ n $ qubits in $\log n $ depth. In a similar manner, we …

Learning about physical systems from quantum-enhanced experiments can outperform
learning from experiments in which only classical memory and processing are available …

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 …

Quantum entanglement is a crucial resource for learning properties from nature, but a
precise characterization of its advantage can be challenging. In this Letter, we consider …

The Heisenberg limit [(HL), with estimation error scales as 1/n] and the standard quantum
limit (SQL,∝ 1/n) are two fundamental limits in estimating an unknown parameter in n copies …

We show that quantum entanglement can provide an exponential advantage in learning
properties of a bosonic continuous-variable (CV) system. The task we consider is estimating …