Zobrazeno 1 - 10
of 13 633
pro vyhledávání: '"ARUNACHALAM, P."'
We consider the problems of testing and learning an unknown $n$-qubit Hamiltonian $H$ from queries to its evolution operator $e^{-iHt}$ under the normalized Frobenius norm. We prove: 1. Local Hamiltonians: We give a tolerant testing protocol to decid
Externí odkaz:
http://arxiv.org/abs/2411.00082
We show an improved inverse theorem for the Gowers-$3$ norm of $n$-qubit quantum states $|\psi\rangle$ which states that: for every $\gamma\geq 0$, if the $\textsf{Gowers}(|\psi \rangle,3)^8 \geq \gamma$ then the stabilizer fidelity of $|\psi\rangle$
Externí odkaz:
http://arxiv.org/abs/2410.22220
We consider the task of distributed inner product estimation when allowed limited quantum communication. Here, Alice and Bob are given $k$ copies of an unknown $n$-qubit quantum states $\vert \psi \rangle,\vert \phi \rangle$ respectively. They are al
Externí odkaz:
http://arxiv.org/abs/2410.12684
Autor:
Huo, Pingyi, Devulapally, Anusha, Maruf, Hasan Al, Park, Minseo, Nair, Krishnakumar, Arunachalam, Meena, Akbulut, Gulsum Gudukbay, Kandemir, Mahmut Taylan, Narayanan, Vijaykrishnan
Deep Learning Recommendation Models (DLRMs) have become increasingly popular and prevalent in today's datacenters, consuming most of the AI inference cycles. The performance of DLRMs is heavily influenced by available bandwidth due to their large vec
Externí odkaz:
http://arxiv.org/abs/2409.16633
Autor:
Gilson, Aidan, Ai, Xuguang, Arunachalam, Thilaka, Chen, Ziyou, Cheong, Ki Xiong, Dave, Amisha, Duic, Cameron, Kibe, Mercy, Kaminaka, Annette, Prasad, Minali, Siddig, Fares, Singer, Maxwell, Wong, Wendy, Jin, Qiao, Keenan, Tiarnan D. L., Hu, Xia, Chew, Emily Y., Lu, Zhiyong, Xu, Hua, Adelman, Ron A., Tham, Yih-Chung, Chen, Qingyu
Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies imple
Externí odkaz:
http://arxiv.org/abs/2409.13902
Autor:
Arunachalam, Srinivasan, Dutt, Arkopal
We consider the following task: suppose an algorithm is given copies of an unknown $n$-qubit quantum state $|\psi\rangle$ promised $(i)$ $|\psi\rangle$ is $\varepsilon_1$-close to a stabilizer state in fidelity or $(ii)$ $|\psi\rangle$ is $\varepsilo
Externí odkaz:
http://arxiv.org/abs/2408.06289
Autor:
Mohammad, Fahim, Arunachalam, Lakshmi, Sadhu, Samanway, Aasman, Boudewijn, Garg, Shweta, Ahmed, Adil, Colman, Silvie, Arunachalam, Meena, Kulkarni, Sudhir, Mirhaji, Parsa
This study proposes the use of Machine Learning models to predict the early onset of sepsis using deidentified clinical data from Montefiore Medical Center in Bronx, NY, USA. A supervised learning approach was adopted, wherein an XGBoost model was tr
Externí odkaz:
http://arxiv.org/abs/2402.03486
Autor:
Mehta, Nikhil, Lorraine, Jonathan, Masson, Steve, Arunachalam, Ramanathan, Bhat, Zaid Pervaiz, Lucas, James, Zachariah, Arun George
When training deep learning models, the performance depends largely on the selected hyperparameters. However, hyperparameter optimization (HPO) is often one of the most expensive parts of model design. Classical HPO methods treat this as a black-box
Externí odkaz:
http://arxiv.org/abs/2406.18630
Autor:
Kushwaha, Abhijit Kumar, Arunachalam, Sankara, Jokinen, Ville, Daniel, Dan, Truscott, Tadd T.
This paper explores the friction forces encountered by droplets on non-wetting surfaces, specifically focusing on superhydrophobic and superheated substrates. Employing a combination of experimental techniques, including inclined plane tests and cant
Externí odkaz:
http://arxiv.org/abs/2405.17923
We consider the problem of learning low-degree quantum objects up to $\varepsilon$-error in $\ell_2$-distance. We show the following results: $(i)$ unknown $n$-qubit degree-$d$ (in the Pauli basis) quantum channels and unitaries can be learned using
Externí odkaz:
http://arxiv.org/abs/2405.10933