Provably trainable rotationally equivariant quantum machine learning
Exploiting the power of quantum computation to realize superior machine learning
algorithms has been a major research focus of recent years, but the prospects of quantum …
algorithms has been a major research focus of recent years, but the prospects of quantum …
[HTML][HTML] Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
Abstract Machine learning tasks are an exciting application for quantum computers, as it has
been proven that they can learn certain problems more efficiently than classical ones …
been proven that they can learn certain problems more efficiently than classical ones …
A novel image classification framework based on variational quantum algorithms
Y Chen - Quantum Information Processing, 2024 - Springer
Image classification is a crucial task in machine learning with widespread practical
applications. The existing classical framework for image classification typically utilizes a …
applications. The existing classical framework for image classification typically utilizes a …
Calibrating the role of entanglement in variational quantum circuits
Entanglement is a key property of quantum computing that separates it from its classical
counterpart; however, its exact role in the performance of quantum algorithms, especially …
counterpart; however, its exact role in the performance of quantum algorithms, especially …
Non-Unitary Quantum Machine Learning
We introduce several novel probabilistic quantum algorithms that overcome the normal
unitary restrictions in quantum machine learning by leveraging the Linear Combination of …
unitary restrictions in quantum machine learning by leveraging the Linear Combination of …
Radio signal classification by adversarially robust quantum machine learning
Radio signal classification plays a pivotal role in identifying the modulation scheme used in
received radio signals, which is essential for demodulation and proper interpretation of the …
received radio signals, which is essential for demodulation and proper interpretation of the …
An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection
A Kadi, A Selamnia, Z Abou El Houda… - IEEE Open Journal …, 2025 - ieeexplore.ieee.org
In today's rapidly evolving cyber landscape, the growing sophistication of attacks, including
the rise of zero-day exploits, poses critical challenges for network intrusion detection …
the rise of zero-day exploits, poses critical challenges for network intrusion detection …
Quantum Transfer Learning with Adversarial Robustness for Classification of High‐Resolution Image Datasets
The application of quantum machine learning to large‐scale high‐resolution image datasets
is not yet possible due to the limited number of qubits and relatively high level of noise in the …
is not yet possible due to the limited number of qubits and relatively high level of noise in the …
Computable Model-Independent Bounds for Adversarial Quantum Machine Learning
By leveraging the principles of quantum mechanics, QML opens doors to novel approaches
in machine learning and offers potential speedup. However, machine learning models are …
in machine learning and offers potential speedup. However, machine learning models are …
Designing Robust Quantum Neural Networks: Exploring Expressibility, Entanglement, and Control Rotation Gate Selection for Enhanced Quantum Models
In this study, we investigated the robustness of Quanvolutional Neural Networks (QuNNs) in
comparison to their classical counterparts, Convolutional Neural Networks (CNNs), against …
comparison to their classical counterparts, Convolutional Neural Networks (CNNs), against …