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 …

We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …

The recent proliferation of NISQ devices has made it imperative to understand their power. In
this work, we define and study the complexity class NISQ, which encapsulates problems that …

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 …

There has been recent promising experimental and theoretical evidence that quantum
computational tools might enhance the precision and efficiency of physical experiments …

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 …

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 …

We show that entangled measurements provide an exponential advantage in sample
complexity for Pauli channel estimation, which is both a fundamental problem and a …

We provide more sample-efficient versions of some basic routines in quantum data analysis,
along with simpler proofs. Particularly, we give a quantum” Threshold Search” algorithm that …

We revisit the problem of Pauli shadow tomography: given copies of an unknown n-qubit
quantum state ρ, estimate Tr (Pρ) for some set of Pauli operators F to within additive error ϵ …