Variational quantum computing offers a flexible computational paradigm with applications in
diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) …

Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic
particles. By applying quantum mechanics, a lot of problems can be solved in a more …

One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

Deep generative models are key-enabling technology to computer vision, text generation,
and large language models. Denoising diffusion probabilistic models (DDPMs) have …

Quantum machine learning (QML) has received increasing attention due to its potential to
outperform classical machine learning methods in problems, such as classification and …

In this pioneering research paper, we present a groundbreaking exploration into the
synergistic fusion of classical and quantum computing paradigms within the realm of …

A quantum algorithm is an algorithm for solving combinatorial optimization problems with
accurate results. The algorithm is based on the integer P1, such that when P is increased …

Several tasks in quantum-information processing involve quantum learning. For example,
quantum sensing, quantum machine learning, and quantum-computer calibration involve …

Methods of artificial intelligence (AI) and especially machine learning (ML) have been
growing ever more complex, and at the same time have more and more impact on people's …

We analyze a quantum version of the Monge–Kantorovich optimal transport problem. The
quantum transport cost related to a Hermitian cost matrix C is minimized over the set of all …