Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Mugel, Samuel"'
Autor:
Tomut, Andrei, Jahromi, Saeed S., Sarkar, Abhijoy, Kurt, Uygar, Singh, Sukhbinder, Ishtiaq, Faysal, Muñoz, Cesar, Bajaj, Prabdeep Singh, Elborady, Ali, del Bimbo, Gianni, Alizadeh, Mehrazin, Montero, David, Martin-Ramiro, Pablo, Ibrahim, Muhammad, Alaoui, Oussama Tahiri, Malcolm, John, Mugel, Samuel, Orus, Roman
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and l
Externí odkaz:
http://arxiv.org/abs/2401.14109
Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector. In this work, we introduce a Tensor Convolutional Neural Network (T-CNN) and examine its performance on a real defect detect
Externí odkaz:
http://arxiv.org/abs/2401.01373
Autor:
Patel, Raj G., Dominguez, Tomas, Dib, Mohammad, Palmer, Samuel, Cadarso, Andrea, Contreras, Fernando De Lope, Ratnani, Abdelkader, Casanova, Francisco Gomez, Hernández-Santana, Senaida, Díaz-Fernández, Álvaro, Andrés, Eva, Luis-Hita, Jorge, Sánchez-Martínez, Escolástico, Mugel, Samuel, Orus, Roman
The Cheyette model is a quasi-Gaussian volatility interest rate model widely used to price interest rate derivatives such as European and Bermudan Swaptions for which Monte Carlo simulation has become the industry standard. In low dimensions, these a
Externí odkaz:
http://arxiv.org/abs/2304.09750
Autor:
Patel, Raj G., Hsing, Chia-Wei, Sahin, Serkan, Palmer, Samuel, Jahromi, Saeed S., Sharma, Shivam, Dominguez, Tomas, Tziritas, Kris, Michel, Christophe, Porte, Vincent, Abid, Mustafa, Aubert, Stephane, Castellani, Pierre, Mugel, Samuel, Orus, Roman
Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted i
Externí odkaz:
http://arxiv.org/abs/2212.14076
Autor:
Leclerc, Lucas, Ortiz-Guitierrez, Luis, Grijalva, Sebastian, Albrecht, Boris, Cline, Julia R. K., Elfving, Vincent E., Signoles, Adrien, Henriet, Loïc, Del Bimbo, Gianni, Sheikh, Usman Ayub, Shah, Maitree, Andrea, Luc, Ishtiaq, Faysal, Duarte, Andoni, Mugel, Samuel, Caceres, Irene, Kurek, Michel, Orus, Roman, Seddik, Achraf, Hammammi, Oumaima, Isselnane, Hacene, M'tamon, Didier
Publikováno v:
Phys. Rev. Research 5, 043117 (2023)
Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithm
Externí odkaz:
http://arxiv.org/abs/2212.03223
Autor:
Palmer, Samuel, Karagiannis, Konstantinos, Florence, Adam, Rodriguez, Asier, Orus, Roman, Naik, Harish, Mugel, Samuel
In this work, we demonstrate how to apply non-linear cardinality constraints, important for real-world asset management, to quantum portfolio optimization. This enables us to tackle non-convex portfolio optimization problems using quantum annealing t
Externí odkaz:
http://arxiv.org/abs/2208.11380
Autor:
Malcolm, John D., Roth, Alexander, Radic, Mladjan, Martin-Ramiro, Pablo, Oillarburu, Jon, Aizpurua, Borja, Orus, Roman, Mugel, Samuel
In this work, we develop a new quantum algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how quantum optimization can play a role in real industrial applications in t
Externí odkaz:
http://arxiv.org/abs/2208.05916
Autor:
Guijo, Daniel, Onofre, Victor, Del Bimbo, Gianni, Mugel, Samuel, Estepa, Daniel, De Carlos, Xabier, Adell, Ana, Lojo, Aizea, Bilbao, Josu, Orus, Roman
In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we consider two approaches: a qu
Externí odkaz:
http://arxiv.org/abs/2208.04988
Autor:
Patel, Raj, Hsing, Chia-Wei, Sahin, Serkan, Jahromi, Saeed S., Palmer, Samuel, Sharma, Shivam, Michel, Christophe, Porte, Vincent, Abid, Mustafa, Aubert, Stephane, Castellani, Pierre, Lee, Chi-Guhn, Mugel, Samuel, Orus, Roman
Partial Differential Equations (PDEs) are used to model a variety of dynamical systems in science and engineering. Recent advances in deep learning have enabled us to solve them in a higher dimension by addressing the curse of dimensionality in new w
Externí odkaz:
http://arxiv.org/abs/2208.02235
Autor:
Sanz-Fernandez, Cristina, Hernandez, Rodrigo, Marciniak, Christian D., Pogorelov, Ivan, Monz, Thomas, Benfenati, Francesco, Mugel, Samuel, Orus, Roman
We present an algorithm which efficiently estimates the intrinsic long-term value of a portfolio of assets on a quantum computer. The method relies on quantum amplitude estimation to estimate the mean of a novel implementation of the Gordon-Shapiro f
Externí odkaz:
http://arxiv.org/abs/2111.14970