The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …

An elegant quantum version of least-square support vector machine, which is exponentially
faster than the classical counterpart, was given by Rebentrost et al. using the matrix …

Quantum optimization algorithms can outperform their classical counterpart and are key in
modern technology. The second-order optimization algorithm (the Newton algorithm) is a …

Support vector machine is a supervised machine learning algorithm, which is usually solved
by iterative method. Quantum algorithms have significant advantages over classical …

Variational quantum algorithms rely on the optimization of parameterized quantum circuits in
noisy settings. The commonly used back-propagation procedure in classical machine …

Combinatorial optimization has wide and high-value applications in many fields of science
and industry, but solving general combinatorial optimization problems is non-deterministic …

The gradient descent approach is the key ingredient in variational quantum algorithms and
machine learning tasks, which is an optimization algorithm for finding a local minimum of an …

Gradient descent is one of the most basic algorithms for solving continuous optimization
problems. In [Jordan, PRL, 95 (5): 050501, 2005], Jordan proposed the first quantum …

In optimization, a pivotal challenge involves minimizing or maximizing polynomial functions,
which is often tackled through iterative methods. Although quantum algorithms hold promise …

Multiqubit CCZ gates form one of the building blocks of quantum algorithms and have been
involved in achieving many theoretical and experimental triumphs. Designing a simple and …