The recent advent of noisy intermediate-scale quantum devices, especially near-term
quantum computers, has sparked extensive research efforts concerned with their possible …

A large ongoing research effort focuses on variational quantum algorithms (VQAs),
representing leading candidates to achieve computational speed-ups on current quantum …

Among different quantum algorithms, PQC for QML show promises on near-term devices. To
facilitate the QML and PQC research, a recent python library called TorchQuantum has been …

For two unknown mixed quantum states and in an-dimensional Hilbert space, computing
their fidelity is a basic problem with many important applications in quantum computing and …

Accurately estimating high-order moments of quantum states is an elementary precondition
for many crucial tasks in quantum computing, such as entanglement spectroscopy, entropy …

We propose a series of quantum algorithms for computing a wide range of quantum
entropies and distances, including the von Neumann entropy, quantum Rényi entropy, trace …

The performance of a quantum information processing protocol is ultimately judged by
distinguishability measures that quantify how distinguishable the actual result of the protocol …

In the field of quantum computing, variational quantum algorithms (VQAs) represent a pivotal
category of quantum solutions across a broad spectrum of applications. These algorithms …

We propose a method of quantum machine learning called quantum mutual information
neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual …

Measuring the distinguishability between quantum states is a basic problem in quantum
information theory. In this paper, we develop optimal quantum algorithms that estimate both …