Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Kyrillidis, Anastasios"'
Solving systems of linear equations is a fundamental problem, but it can be computationally intensive for classical algorithms in high dimensions. Existing quantum algorithms can achieve exponential speedups for the quantum linear system problem (QLS
Externí odkaz:
http://arxiv.org/abs/2406.13879
Publikováno v:
Machine Learning (2024): 1-19
Low precision training can significantly reduce the computational overhead of training deep neural networks (DNNs). Though many such techniques exist, cyclic precision training (CPT), which dynamically adjusts precision throughout training according
Externí odkaz:
http://arxiv.org/abs/2403.02243
Autor:
Zhang, Zewen, Paredes, Roger, Sundar, Bhuvanesh, Quiroga, David, Kyrillidis, Anastasios, Duenas-Osorio, Leonardo, Pagano, Guido, Hazzard, Kaden R. A.
The SAT problem is a prototypical NP-complete problem of fundamental importance in computational complexity theory with many applications in science and engineering; as such, it has long served as an essential benchmark for classical and quantum algo
Externí odkaz:
http://arxiv.org/abs/2402.02585
Publikováno v:
2023 IEEE International Conference on Rebooting Computing (ICRC) 118-127
We propose a non-convex optimization algorithm, based on the Burer-Monteiro (BM) factorization, for the quantum process tomography problem, in order to estimate a low-rank process matrix $\chi$ for near-unitary quantum gates. In this work, we compare
Externí odkaz:
http://arxiv.org/abs/2312.01311
Principal Component Analysis (PCA) aims to find subspaces spanned by the so-called principal components that best represent the variance in the dataset. The deflation method is a popular meta-algorithm that sequentially finds individual principal com
Externí odkaz:
http://arxiv.org/abs/2310.04283
Autor:
Dun, Chen, Pan, Qiutai, Jin, Shikai, Stevens, Ria, Miller, Mitchell D., Phillips, Jr., George N., Kyrillidis, Anastasios
Determining the structure of a protein has been a decades-long open question. A protein's three-dimensional structure often poses nontrivial computation costs, when classical simulation algorithms are utilized. Advances in the transformer neural netw
Externí odkaz:
http://arxiv.org/abs/2310.03899
Autor:
Dun, Chen, Garcia, Mirian Hipolito, Zheng, Guoqing, Awadallah, Ahmed Hassan, Kyrillidis, Anastasios, Sim, Robert
Large Language Models (LLMs) have the ability to solve a variety of tasks, such as text summarization and mathematical questions, just out of the box, but they are often trained with a single task in mind. Due to high computational costs, the current
Externí odkaz:
http://arxiv.org/abs/2310.02842
Autor:
Quintero-Peña, Carlos, Thomason, Wil, Kingston, Zachary, Kyrillidis, Anastasios, Kavraki, Lydia E.
Motion planning under sensing uncertainty is critical for robots in unstructured environments to guarantee safety for both the robot and any nearby humans. Most work on planning under uncertainty does not scale to high-dimensional robots such as mani
Externí odkaz:
http://arxiv.org/abs/2309.16862
Advances in Semi-Supervised Learning (SSL) have almost entirely closed the gap between SSL and Supervised Learning at a fraction of the number of labels. However, recent performance improvements have often come \textit{at the cost of significantly in
Externí odkaz:
http://arxiv.org/abs/2309.03469
We carefully evaluate a number of algorithms for learning in a federated environment, and test their utility for a variety of image classification tasks. We consider many issues that have not been adequately considered before: whether learning over d
Externí odkaz:
http://arxiv.org/abs/2309.03237