Zobrazeno 1 - 10
of 348
pro vyhledávání: '"YORDANOV, Yordan"'
The real world obeys quantum physics and quantum computing presents an alternative way to map physical problems to systems that follow the same laws. Such computation fundamentally constitutes a better way to understand the most challenging quantum p
Externí odkaz:
http://arxiv.org/abs/2410.12733
Autor:
Salvatori, Tommaso, Song, Yuhang, Yordanov, Yordan, Millidge, Beren, Xu, Zhenghua, Sha, Lei, Emde, Cornelius, Bogacz, Rafal, Lukasiewicz, Thomas
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply changing the temporal schedulin
Externí odkaz:
http://arxiv.org/abs/2212.00720
Autor:
Dalton, Kieran, Long, Christopher K., Yordanov, Yordan S., Smith, Charles G., Barnes, Crispin H. W., Mertig, Normann, Arvidsson-Shukur, David R. M.
Publikováno v:
npj Quantum Inf 10, 18 (2024)
Variational quantum eigensolvers (VQEs) are leading candidates to demonstrate near-term quantum advantage. Here, we conduct density-matrix simulations of leading gate-based VQEs for a range of molecules. We numerically quantify their level of tolerab
Externí odkaz:
http://arxiv.org/abs/2211.04505
Autor:
Pinchetti, Luca, Salvatori, Tommaso, Yordanov, Yordan, Millidge, Beren, Song, Yuhang, Lukasiewicz, Thomas
A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation (BP). A prominent example is predictive coding (PC), which is a neuroscience-inspired m
Externí odkaz:
http://arxiv.org/abs/2211.03481
Transformers have become an indispensable module for text generation models since their great success in machine translation. Previous works attribute the~success of transformers to the query-key-value dot-product attention, which provides a robust i
Externí odkaz:
http://arxiv.org/abs/2210.03985
Autor:
Salaroglio, Iris C., Stefanova, Denitsa, Teixeira, Ricardo G., Oliveira, Nuno F.B., Ahmed, Amer, Fusi, Fabio, Tzankova, Virginia, Yordanov, Yordan, Machuqueiro, Miguel, Saponara, Simona, Valente, Andreia, Riganti, Chiara
Publikováno v:
In Pharmacological Research October 2024 208
Training a model to provide natural language explanations (NLEs) for its predictions usually requires the acquisition of task-specific NLEs, which is time- and resource-consuming. A potential solution is the few-shot out-of-domain transfer of NLEs fr
Externí odkaz:
http://arxiv.org/abs/2112.06204
Autor:
Armaos, V., Badounas, Dimitrios A., Deligiannis, Paraskevas, Lianos, Konstantinos, Yordanov, Yordan S.
Computational chemistry is one of the most promising applications of quantum computing, mostly thanks to the development of the Variational Quantum Eigensolver (VQE) algorithm. VQE is being studied extensively and numerous optimisations of VQE's sub-
Externí odkaz:
http://arxiv.org/abs/2110.12756
Calculations of molecular spectral properties, like photodissociation rates and absorption bands, rely on knowledge of the excited state energies of the molecule of interest. Protocols based on the variational quantum eigensolver (VQE) are promising
Externí odkaz:
http://arxiv.org/abs/2106.06296
Molecular simulations with the variational quantum eigensolver (VQE) are a promising application for emerging noisy intermediate-scale quantum computers. Constructing accurate molecular ans\"atze that are easy to optimize and implemented by shallow q
Externí odkaz:
http://arxiv.org/abs/2011.10540