The task of learning a quantum circuit to prepare a given mixed state is a fundamental
quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed …

To improve data analysis and feature learning, this study compares the effectiveness of
quantum dimensionality reduction (qDR) techniques to classical ones. In this study, we …

The Hamiltonian of an isolated quantum-mechanical system determines its dynamics and
physical behavior. This study investigates the possibility of learning and utilizing a system's …

Quantum-enhanced data science, also known as quantum machine learning (QML), is of
growing interest as an application of near-term quantum computers. Variational QML …

Isometric feature mapping (Isomap) is a nonlinear dimensionality reduction algorithm based
on the concept of manifold learning. The algorithm is widely used in neuroimaging, spectral …

Quantum Machine Learning has gained significant attention in recent years as a way to
leverage the relationship between quantum information and machine learning. Principal …

This study explores the potential of quantum machine learning and quantum computing for
climate change detection, climate modeling, and climate digital twin. We additionally …

Principal component analysis (PCA) is an important dimensionality reduction method in
machine learning and data analysis. Recently, the quantum version of PCA has been …

In quantum learning tasks, quantum memory can offer exponential reductions in statistical
complexity compared to any single-copy strategies, but this typically necessitates at least …

Biomarkers play a central role in medicine's gradual progress towards proactive,
personalized precision diagnostics and interventions. However, finding biomarkers that …