Let quantum neural networks choose their own frequencies
B Jaderberg, AA Gentile, YA Berrada, E Shishenina… - Physical Review A, 2024 - APS
Parameterized quantum circuits as machine learning models are typically well described by
their representation as a partial Fourier series of the input features, with frequencies …
their representation as a partial Fourier series of the input features, with frequencies …
Quantum Kernel Methods under Scrutiny: A Benchmarking Study
J Schnabel, M Roth - arXiv preprint arXiv:2409.04406, 2024 - arxiv.org
Since the entry of kernel theory in the field of quantum machine learning, quantum kernel
methods (QKMs) have gained increasing attention with regard to both probing promising …
methods (QKMs) have gained increasing attention with regard to both probing promising …
Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule
The study of variational quantum algorithms (VQCs) has received significant attention from
the quantum computing community in recent years. These hybrid algorithms, utilizing both …
the quantum computing community in recent years. These hybrid algorithms, utilizing both …
QNN Learning Algorithm to Diversify the Framework in Deep Learning
The QNN (quantum neural network) algorithmic approach is to interpreted the forward
networking in the dataset. Alike, some classical dataset structure take input from the layer of …
networking in the dataset. Alike, some classical dataset structure take input from the layer of …
Performance Analysis of Quantum Federated Learning in Data Classification
H Lee, S Park - 한국통신학회논문지, 2024 - dbpia.co.kr
Federated learning is a method with the advantage of allowing various institutions to create
a global model by sharing model parameters without sharing the data they possess. Also …
a global model by sharing model parameters without sharing the data they possess. Also …