Hybrid quantum neural network for drug response prediction
Simple Summary This work successfully employs a novel approach in processing patient
and drug data to predict the drug response for cancer patients. The approach uses a deep …
and drug data to predict the drug response for cancer patients. The approach uses a deep …
Multiclass seismic damage detection of buildings using quantum convolutional neural network
The traditional visual inspection technique for damage assessment of buildings immediately
after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies …
after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies …
Hybrid quantum-classical convolutional neural network model for image classification
Image classification plays an important role in remote sensing. Earth observation (EO) has
inevitably arrived in the big data era, but the high requirement on computation power has …
inevitably arrived in the big data era, but the high requirement on computation power has …
Quantum-inspired machine learning: a survey
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention
from researchers for its potential to leverage principles of quantum mechanics within …
from researchers for its potential to leverage principles of quantum mechanics within …
Quantum convolutional neural networks for multi-channel supervised learning
As the rapidly evolving field of machine learning continues to produce incredibly useful tools
and models, the potential for quantum computing to provide speed up for machine learning …
and models, the potential for quantum computing to provide speed up for machine learning …
Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis
L Lusnig, A Sagingalieva, M Surmach, T Protasevich… - Diagnostics, 2024 - mdpi.com
In the realm of liver transplantation, accurately determining hepatic steatosis levels is crucial.
Recognizing the essential need for improved diagnostic precision, particularly for optimizing …
Recognizing the essential need for improved diagnostic precision, particularly for optimizing …
Hybrid Quantum Deep Learning With Superpixel Encoding for Earth Observation Data Classification
Earth observation (EO) has inevitably entered the Big Data era. The computational
challenge associated with analyzing large EO data using sophisticated deep learning …
challenge associated with analyzing large EO data using sophisticated deep learning …
Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers
When applying quantum computing to machine learning tasks, one of the first considerations
is the design of the quantum machine learning model itself. Conventionally, the design of …
is the design of the quantum machine learning model itself. Conventionally, the design of …
Let the Quantum Creep In: Designing Quantum Neural Network Models by Gradually Swapping Out Classical Components
Artificial Intelligence (AI), with its multiplier effect and wide applications in multiple areas,
could potentially be an important application of quantum computing. Since modern AI …
could potentially be an important application of quantum computing. Since modern AI …
A Study on Quantum Neural Networks in Healthcare 5.0
S Chakraborty - arXiv preprint arXiv:2412.06818, 2024 - arxiv.org
The working environment in healthcare analytics is transforming with the emergence of
healthcare 5.0 and the advancements in quantum neural networks. In addition to analyzing …
healthcare 5.0 and the advancements in quantum neural networks. In addition to analyzing …