Quantum neural networks (QNNs) and quantum kernels stand as prominent fi...
Understanding the dynamics of large quantum systems is hindered by the c...
Entanglement serves as the resource to empower quantum computing. Recent...
In the task of speaker diarization, the number of small-scale meetings
a...
The variational quantum eigensolver (VQE) is a leading strategy that exp...
This paper describes the BUCEA speaker diarization system for the 2022
V...
Many fundamental properties of a quantum system are captured by its
Hami...
Quantum computers are next-generation devices that hold promise to perfo...
Most recent self-supervised learning methods learn visual representation...
The intrinsic probabilistic nature of quantum mechanics invokes endeavor...
Efficient quantum compiling tactics greatly enhance the capability of qu...
Structural re-parameterization (Rep) methods achieve noticeable improvem...
Algorithms which minimize the averaged loss have been widely designed fo...
Quantum auto-encoder (QAE) is a powerful tool to relieve the curse of
di...
Variational quantum algorithms (VQAs) have the potential of utilizing
ne...
The core of quantum machine learning is to devise quantum models with go...
A key problem in the field of quantum computing is understanding whether...
Quantum error mitigation techniques are at the heart of quantum computat...
Quantum machine learning is expected to be one of the first practical
ap...
Differentially private (DP) learning, which aims to accurately extract
p...
Noise in quantum information processing is often viewed as a disruptive ...
A wide range of fundamental machine learning tasks that are addressed by...
The exploration of quantum algorithms that possess quantum advantages is...
Parameterized quantum circuits (PQCs) have been broadly used as a hybrid...
In this Letter, we propose a quantum machine learning scheme for the
cla...