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

Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …

Electric power systems provide the backbone of modern industrial societies. Enabling
scalable grid analytics is the keystone to successfully operating large transmission and …

Our world, of course, changed in many other ways as well since the publication of the first
edition. The global pandemic impacted all areas of society and will probably transform how …

The quantum state preparation problem aims to prepare an-qubit quantum state from the
initial state, for a given unit vector with. The problem is of fundamental importance in …

Variational hybrid quantum classical algorithms are a class of quantum algorithms intended
to run on noisy intermediate-scale quantum (NISQ) devices. These algorithms employ a …

This is a set of lecture notes used in a graduate topic class in applied mathematics
called``Quantum Algorithms for Scientific Computation''at the Department of Mathematics …

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 …

Quantum machine learning is at the intersection of two of the most sought after research
areas—quantum computing and classical machine learning. Quantum machine learning …

After learning basic quantum computing concepts, it is desirable to reinforce the learning
using an important and relatively complex algorithm through which students can observe …