Zobrazeno 1 - 10
of 16 669
pro vyhledávání: '"Economou, A."'
Autor:
Tang, Ho Lun, Chen, Yanzhu, Biswas, Prakriti, Magann, Alicia B., Arenz, Christian, Economou, Sophia E.
We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient measurements dete
Externí odkaz:
http://arxiv.org/abs/2411.09736
Autor:
Mullinax, J. Wayne, Anastasiou, Panagiotis G., Larson, Jeffrey, Economou, Sophia E., Tubman, Norm M.
The ADAPT-VQE algorithm is a promising method for generating a compact ansatz based on derivatives of the underlying cost function, and it yields accurate predictions of electronic energies for molecules. In this work we report the implementation and
Externí odkaz:
http://arxiv.org/abs/2411.07920
Autor:
Barnes, Edwin, Bennett, Michael B., Boltasseva, Alexandra, Borish, Victoria, Brown, Bennett, Carr, Lincoln D., Ceballos, Russell R., Dukes, Faith, Easton, Emily W., Economou, Sophia E., Edwards, E. E., Finkelstein, Noah D., Fracchiolla, C., Franklin, Diana, Freericks, J. K., Goss, Valerie, Hannum, Mark, Holincheck, Nancy, Kelly, Angela M., Lanes, Olivia, Lewandowski, H. J., Matsler, Karen Jo, Mercurio, Emily, Montaño, Inès, Murdock, Maajida, Peltz, Kiera, Perron, Justin K., Richardson, Christopher J. K., Rosenberg, Jessica L., Ross, Richard S., Ryu, Minjung, Samuel, Raymond E., Schrode, Nicole, Schwamberger, Susan, Searles, Thomas A., Singh, Chandralekha, Tingle, Alexandra, Zwickl, Benjamin M.
In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored worksho
Externí odkaz:
http://arxiv.org/abs/2410.23460
Autor:
Konti, Xenia, Riess, Hans, Giannopoulos, Manos, Shen, Yi, Pencina, Michael J., Economou-Zavlanos, Nicoleta J., Zavlanos, Michael M.
In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clustered Federated Learning (CS-RCFL). Our approach leverages the Wasserst
Externí odkaz:
http://arxiv.org/abs/2410.07039
Network science has presented community detection as a valuable tool for revealing the functional modules in complex systems as rooted in the wiring architectures of complex networks. The varying procedures of community detection can produce, however
Externí odkaz:
http://arxiv.org/abs/2409.12852
Autor:
Gustafson, Erik, Sherbert, Kyle, Florio, Adrien, Shirali, Karunya, Chen, Yanzhu, Lamm, Henry, Valgushev, Semeon, Weichselbaum, Andreas, Economou, Sophia E., Pisarski, Robert D., Tubman, Norm M.
Inspired by recent advancements of simulating periodic systems on quantum computers, we develop a new approach, (SC)$^2$-ADAPT-VQE, to further advance the simulation of these systems. Our approach extends the scalable circuits ADAPT-VQE framework, wh
Externí odkaz:
http://arxiv.org/abs/2408.12641
Autor:
Lei, Mi, Fukumori, Rikuto, Wu, Chun-Ju, Barnes, Edwin, Economou, Sophia, Choi, Joonhee, Faraon, Andrei
Studying and controlling quantum many-body interactions is fundamentally important for quantum science and related emerging technologies. Optically addressable solid-state spins offer a promising platform for exploring various quantum many-body pheno
Externí odkaz:
http://arxiv.org/abs/2408.00252
We propose a generative quantum learning algorithm, R\'enyi-ADAPT, using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be minimized is the maximal quantum R\'enyi divergence of order two, an
Externí odkaz:
http://arxiv.org/abs/2408.00218
Photonic graph states are important for measurement- and fusion-based quantum computing, quantum networks, and sensing. They can in principle be generated deterministically by using emitters to create the requisite entanglement. Finding ways to minim
Externí odkaz:
http://arxiv.org/abs/2407.15777
Autor:
Economou, Theo, Parliari, Daphne, Tobias, Aurelio, Dawkins, Laura, Stoner, Oliver, Steptoe, Hamish, Lowe, Rachel, Athanasiadou, Maria, Sarran, Christophe, Lelieveld, Jos
We present a statistical modelling framework for implementing Distributed Lag Models (DLMs), encompassing several extensions of the approach to capture the temporally distributed effect from covariates via regression. We place DLMs in the context of
Externí odkaz:
http://arxiv.org/abs/2407.13374