As quantum computers become available to the general public, the need has arisen to train
a cohort of quantum programmers, many of whom have been developing classical computer …

Recently, several quantum machine learning algorithms have been proposed that may offer
quantum speed-ups over their classical counterparts. Most of these algorithms are either …

We present an algorithmic framework for quantum-inspired classical algorithms on close-to-
low-rank matrices, generalizing the series of results started by Tang's breakthrough quantum …

A central roadblock to analyzing quantum algorithms on quantum states is the lack of a
comparable input model for classical algorithms. Inspired by recent work of the author [E …

The Quantum Singular Value Transformation (QSVT) is a recent technique that gives a
unified framework to describe most quantum algorithms discovered so far, and may lead to …

We give a classical algorithm for linear regression analogous to the quantum matrix
inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters' 09] for low-rank …

In the context of optical signal processing, quantum and quantum-inspired machine learning
algorithms have massive potential for deployment. One of the applications is in error …

We develop three new methods to implement any Linear Combination of Unitaries (LCU), a
powerful quantum algorithmic tool with diverse applications. While the standard LCU …

The field of quantum machine learning (QML) produces many proposals for attaining
quantum speedups for tasks in machine learning and data analysis. Such speedups can …

We establish an improved classical algorithm for solving linear systems in a model
analogous to the QRAM that is used by quantum linear solvers. Precisely, for the linear …