Machine learning in nuclear physics at low and intermediate energies
Abstract Machine learning (ML) is becoming a new paradigm for scientific research in
various research fields due to its exciting and powerful capability of modeling tools used for …
various research fields due to its exciting and powerful capability of modeling tools used for …
Recent advances for quantum neural networks in generative learning
Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …
beyond the reach of classical computers. A leading method towards achieving this goal is …
Experimental quantum adversarial learning with programmable superconducting qubits
W Ren, W Li, S Xu, K Wang, W Jiang, F **… - Nature Computational …, 2022 - nature.com
Quantum computing promises to enhance machine learning and artificial intelligence.
However, recent theoretical works show that, similar to traditional classifiers based on deep …
However, recent theoretical works show that, similar to traditional classifiers based on deep …
Variational quantum reinforcement learning via evolutionary optimization
Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …
a promising direction for performing RL on a quantum computer. However, potential …
Problem-dependent power of quantum neural networks on multiclass classification
Quantum neural networks (QNNs) have become an important tool for understanding the
physical world, but their advantages and limitations are not fully understood. Some QNNs …
physical world, but their advantages and limitations are not fully understood. Some QNNs …
Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning
D Bokhan, AS Mastiukova, AS Boev… - Frontiers in …, 2022 - frontiersin.org
Multiclass classification is of great interest for various applications, for example, it is a
common task in computer vision, where one needs to categorize an image into three or …
common task in computer vision, where one needs to categorize an image into three or …
Trainability enhancement of parameterized quantum circuits via reduced-domain parameter initialization
Parameterized quantum circuits (PQCs) have been widely used as a machine learning
model to explore the potential of achieving quantum advantages for various tasks. However …
model to explore the potential of achieving quantum advantages for various tasks. However …
Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications
The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
Advances in Quantum Machine Learning and Deep learning for image classification: a Survey
Image classification, which is a fundamental element of Computer Vision (CV) and Artificial
Intelligence (AI), has been researched intensively in numerous domains and embedded in …
Intelligence (AI), has been researched intensively in numerous domains and embedded in …
Statistical analysis of quantum state learning process in quantum neural networks
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term
quantum advantage in various fields, where many applications can be viewed as learning a …
quantum advantage in various fields, where many applications can be viewed as learning a …