As we begin to reach the limits of classical computing, quantum computing has emerged as
a technology that has captured the imagination of the scientific world. While for many years …

The power of quantum computing technologies is based on the fundamentals of quantum
mechanics, such as quantum superposition, quantum entanglement, or the no-cloning …

Neural network-based machine learning has recently proven successful for many complex
applications ranging from image recognition to precision medicine. However, its direct …

Many quantum algorithms have daunting resource requirements when compared to what is
available today. To address this discrepancy, a quantum-classical hybrid optimization …

The preparation of quantum states using short quantum circuits is one of the most promising
near-term applications of small quantum computers, especially if the circuit is short enough …

Classical autoencoders are neural networks that can learn efficient low-dimensional
representations of data in higher-dimensional space. The task of an autoencoder is, given …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Abstract Machine learning is a promising application of quantum computing, but challenges
remain for implementation today because near-term devices have a limited number of …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Generative modeling, which learns joint probability distribution from data and generates
samples according to it, is an important task in machine learning and artificial intelligence …