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Quantum Support Vector Machine for Classification Task: A Review
Journal of Multiscale Materials InformaticsQuantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained ...
Muhamad Akrom
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IEEE Access
Skin diseases affect millions of people worldwide, leading to significant healthcare burdens and challenges in diagnosis and treatment. In the past few years, machine learning techniques have demonstrated potential in assisting dermatologists with ...
S. Reka +4 more
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Skin diseases affect millions of people worldwide, leading to significant healthcare burdens and challenges in diagnosis and treatment. In the past few years, machine learning techniques have demonstrated potential in assisting dermatologists with ...
S. Reka +4 more
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Classification of the Fashion-MNIST Dataset on a Quantum Computer
arXiv.orgThe potential impact of quantum machine learning algorithms on industrial applications remains an exciting open question. Conventional methods for encoding classical data into quantum computers are not only too costly for a potential quantum advantage in
Kevin Shen +3 more
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Quantum classification algorithm with multi-class parallel training
Quantum Information Processing, 2022Anqi Zhang, Xiaoyun He, Shengmei Zhao
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Quantum Classification of Malware
2014The D-Wave architecture is a unique approach to computing which utilizes quantum annealing to solve discrete optimization problems. Applications for D-Wave machines include binary classification, complex protein-folding models, and heuristics for intractable problems such as the Traveling Salesman Problem.
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Quantum‐Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security
Advanced Quantum TechnologiesQuantum Machine Learning (QML) leverages the transformative power of quantum computing to explore a broad range of applications, including optimization, data analysis, and complex problem‐solving.
Muhammed Yusuf Küçükkara +2 more
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Deep Quantum Networks for Classification
2010 20th International Conference on Pattern Recognition, 2010This paper introduces a new type of deep learning method named Deep Quantum Network (DQN) for classification. DQN inherits the capability of modeling the structure of a feature space by fuzzy sets. At first, we propose the architecture of DQN, which consists of quantum neuron and sigmoid neuron and can guide the embedding of samples divisible in new ...
Shusen Zhou, Qingcai Chen, Xiaolong Wang
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Hamiltonian Identification via Quantum Ensemble Classification
IEEE Transactions on Neural Networks and Learning SystemsIdentifying the Hamiltonian of an unknown quantum system is a critical task in the area of quantum information. In this article, we propose a systematic Hamiltonian identification approach via quantum ensemble multiclass classification (HI-QEMC). This approach is implemented by a three-step iterative refining process, i.e., parameter interval guess ...
Haixu Yu +3 more
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Quantum guidelines for solid-state spin defects
Nature Reviews Materials, 2021Gary Wolfowicz +2 more
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