Academic literature on the topic 'Machine Learning, Deep Learning, Quantum Computing, Network Theory'
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Journal articles on the topic "Machine Learning, Deep Learning, Quantum Computing, Network Theory"
Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum deep learning." Quantum Information and Computation 16, no. 7&8 (May 2016): 541–87. http://dx.doi.org/10.26421/qic16.7-8-1.
Full textCrawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines." Quantum Information and Computation 18, no. 1&2 (February 2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.
Full textMercaldo, Francesco, Giovanni Ciaramella, Giacomo Iadarola, Marco Storto, Fabio Martinelli, and Antonella Santone. "Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification." Applied Sciences 12, no. 23 (November 24, 2022): 12025. http://dx.doi.org/10.3390/app122312025.
Full textVijayasekaran, G., and M. Duraipandian. "Resource scheduling in edge computing IoT networks using hybrid deep learning algorithm." System research and information technologies, no. 3 (October 30, 2022): 86–101. http://dx.doi.org/10.20535/srit.2308-8893.2022.3.06.
Full textGianani, Ilaria, and Claudia Benedetti. "Multiparameter estimation of continuous-time quantum walk Hamiltonians through machine learning." AVS Quantum Science 5, no. 1 (March 2023): 014405. http://dx.doi.org/10.1116/5.0137398.
Full textDing, Li, Haowen Wang, Yinuo Wang, and Shumei Wang. "Based on Quantum Topological Stabilizer Color Code Morphism Neural Network Decoder." Quantum Engineering 2022 (July 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/9638108.
Full textGhavasieh, A., and M. De Domenico. "Statistical physics of network structure and information dynamics." Journal of Physics: Complexity 3, no. 1 (January 26, 2022): 011001. http://dx.doi.org/10.1088/2632-072x/ac457a.
Full textOkey, Ogobuchi Daniel, Siti Sarah Maidin, Renata Lopes Rosa, Waqas Tariq Toor, Dick Carrillo Melgarejo, Lunchakorn Wuttisittikulkij, Muhammad Saadi, and Demóstenes Zegarra Rodríguez. "Quantum Key Distribution Protocol Selector Based on Machine Learning for Next-Generation Networks." Sustainability 14, no. 23 (November 29, 2022): 15901. http://dx.doi.org/10.3390/su142315901.
Full textOkuboyejo, Damilola A., and Oludayo O. Olugbara. "Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning." Algorithms 15, no. 12 (November 24, 2022): 443. http://dx.doi.org/10.3390/a15120443.
Full textLi, Jian, and Yongyan Zhao. "Construction of Innovation and Entrepreneurship Platform Based on Deep Learning Algorithm." Scientific Programming 2021 (December 9, 2021): 1–7. http://dx.doi.org/10.1155/2021/1833979.
Full textDissertations / Theses on the topic "Machine Learning, Deep Learning, Quantum Computing, Network Theory"
Malmgren, Henrik. "Revision of an artificial neural network enabling industrial sorting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392690.
Full textBuffoni, Lorenzo. "Machine learning applications in science." Doctoral thesis, 2021. http://hdl.handle.net/2158/1227616.
Full textScellier, Benjamin. "A deep learning theory for neural networks grounded in physics." Thesis, 2020. http://hdl.handle.net/1866/25593.
Full textIn the last decade, deep learning has become a major component of artificial intelligence, leading to a series of breakthroughs across a wide variety of domains. The workhorse of deep learning is the optimization of loss functions by stochastic gradient descent (SGD). Traditionally in deep learning, neural networks are differentiable mathematical functions, and the loss gradients required for SGD are computed with the backpropagation algorithm. However, the computer architectures on which these neural networks are implemented and trained suffer from speed and energy inefficiency issues, due to the separation of memory and processing in these architectures. To solve these problems, the field of neuromorphic computing aims at implementing neural networks on hardware architectures that merge memory and processing, just like brains do. In this thesis, we argue that building large, fast and efficient neural networks on neuromorphic architectures also requires rethinking the algorithms to implement and train them. We present an alternative mathematical framework, also compatible with SGD, which offers the possibility to design neural networks in substrates that directly exploit the laws of physics. Our framework applies to a very broad class of models, namely those whose state or dynamics are described by variational equations. This includes physical systems whose equilibrium state minimizes an energy function, and physical systems whose trajectory minimizes an action functional (principle of least action). We present a simple procedure to compute the loss gradients in such systems, called equilibrium propagation (EqProp), which requires solely locally available information for each trainable parameter. Since many models in physics and engineering can be described by variational principles, our framework has the potential to be applied to a broad variety of physical systems, whose applications extend to various fields of engineering, beyond neuromorphic computing.
Book chapters on the topic "Machine Learning, Deep Learning, Quantum Computing, Network Theory"
S., Karthigai Selvi. "Structural and Functional Data Processing in Bio-Computing and Deep Learning." In Structural and Functional Aspects of Biocomputing Systems for Data Processing, 198–215. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6523-3.ch010.
Full textConference papers on the topic "Machine Learning, Deep Learning, Quantum Computing, Network Theory"
Buiu, Catalin, and Vladrares Danaila. "DATA SCIENCE AND MACHINE LEARNING TECHNIQUES FOR CASE-BASED LEARNING IN MEDICAL BIOENGINEERING EDUCATION." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-194.
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