Quantum computers have made extraordinary progress over the past decade, and
significant milestones have been achieved along the path of pursuing universal fault-tolerant …

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 …

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 …

We study the practical performance of quantum-inspired algorithms for recommendation
systems and linear systems of equations. These algorithms were shown to have an …

Support vector machine (SVM) is a particularly powerful and flexible supervised learning
model that analyzes data for both classification and regression, whose usual algorithm …

The search for an application of near-term quantum devices is widespread. Quantum
machine learning is touted as a potential utilisation of such devices, particularly those out of …

Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade, and achieve disruptive impact on numerous industry sectors …

We present a quantum eigenstate filtering algorithm based on quantum signal processing
(QSP) and minimax polynomials. The algorithm allows us to efficiently prepare a target …

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 …