Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

The ability of quantum computers to directly manipulate and analyze quantum states stored
in quantum memory allows them to learn about aspects of our physical world that would …

Despite fundamental interests in learning quantum circuits, the existence of a
computationally efficient algorithm for learning shallow quantum circuits remains an open …

In this paper, we present a learning algorithm aimed at learning states obtained from
computational basis states by Clifford circuits doped with a finite number $ t $ of $ T $-gates …

We give a pair of algorithms that efficiently learn a quantum state prepared by Clifford gates
and $ O (\log n) $ non-Clifford gates. Specifically, for an $ n $-qubit state $|\psi\rangle …

Pseudorandom quantum states (PRSs) and pseudorandom unitaries (PRUs) possess the
dual nature of being efficiently constructible while appearing completely random to any …

Notions of so-called magic quantify how non-classical quantum states are in a precise
sense: low values of magic preclude quantum advantage; they also play a key role in …

One of the primary objectives in the field of quantum state learning is to develop algorithms
that are time-efficient for learning states generated from quantum circuits. Earlier …

We present a novel classical algorithm designed to learn the stabilizer group--namely the
group of Pauli strings for which a state is a $\pm 1$ eigenvector--of a given Matrix Product …

We show an improved inverse theorem for the Gowers-$3 $ norm of $ n $-qubit quantum
states $|\psi\rangle $ which states that: for every $\gamma\geq 0$, if the $\textsf …