Generating Approximate Ground States of Strongly Correlated Quantum Many-Body Systems Through Quantum Imaginary Time Evolution

Autor: Kaicher, Michael P., Dommert, Florian, Wever, Christopher, Amsler, Maximilian, Kühn, Michael

Most quantum algorithms designed to generate or probe properties of the ground state of a quantum many-body system require as input an initial state with a large overlap with the desired ground state. One approach for preparing such a ground state is

Externí odkaz: http://arxiv.org/abs/2409.01320

ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

Autor: Margraf, Valentin, Wever, Marcel, Gilhuber, Sandra, Tavares, Gabriel Marques, Seidl, Thomas, Hüllermeier, Eyke

In settings where only a budgeted amount of labeled data can be afforded, active learning seeks to devise query strategies for selecting the most informative data points to be labeled, aiming to enhance learning algorithms' efficiency and performance

Externí odkaz: http://arxiv.org/abs/2406.17322

Mining patterns in syntax trees to automate code reviews of student solutions for programming exercises

Autor: Van Petegem, Charlotte, Demeyere, Kasper, Maertens, Rien, Strijbol, Niko, De Wever, Bram, Mesuere, Bart, Dawyndt, Peter

In programming education, providing manual feedback is essential but labour-intensive, posing challenges in consistency and timeliness. We introduce ECHO, a machine learning method to automate the reuse of feedback in educational code reviews by anal

Externí odkaz: http://arxiv.org/abs/2405.01579

Automated Machine Learning for Multi-Label Classification

Autor: Wever, Marcel

Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably binary and multinomial classifica

Externí odkaz: http://arxiv.org/abs/2402.18198

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Autor: Alexeev, Yuri, Amsler, Maximilian, Baity, Paul, Barroca, Marco Antonio, Bassini, Sanzio, Battelle, Torey, Camps, Daan, Casanova, David, Choi, Young Jai, Chong, Frederic T., Chung, Charles, Codella, Chris, Corcoles, Antonio D., Cruise, James, Di Meglio, Alberto, Dubois, Jonathan, Duran, Ivan, Eckl, Thomas, Economou, Sophia, Eidenbenz, Stephan, Elmegreen, Bruce, Fare, Clyde, Faro, Ismael, Fernández, Cristina Sanz, Ferreira, Rodrigo Neumann Barros, Fuji, Keisuke, Fuller, Bryce, Gagliardi, Laura, Galli, Giulia, Glick, Jennifer R., Gobbi, Isacco, Gokhale, Pranav, Gonzalez, Salvador de la Puente, Greiner, Johannes, Gropp, Bill, Grossi, Michele, Gull, Emanuel, Healy, Burns, Huang, Benchen, Humble, Travis S., Ito, Nobuyasu, Izmaylov, Artur F., Javadi-Abhari, Ali, Jennewein, Douglas, Jha, Shantenu, Jiang, Liang, Jones, Barbara, de Jong, Wibe Albert, Jurcevic, Petar, Kirby, William, Kister, Stefan, Kitagawa, Masahiro, Klassen, Joel, Klymko, Katherine, Koh, Kwangwon, Kondo, Masaaki, Kurkcuoglu, Doga Murat, Kurowski, Krzysztof, Laino, Teodoro, Landfield, Ryan, Leininger, Matt, Leyton-Ortega, Vicente, Li, Ang, Lin, Meifeng, Liu, Junyu, Lorente, Nicolas, Luckow, Andre, Martiel, Simon, Martin-Fernandez, Francisco, Martonosi, Margaret, Marvinney, Claire, Medina, Arcesio Castaneda, Merten, Dirk, Mezzacapo, Antonio, Michielsen, Kristel, Mitra, Abhishek, Mittal, Tushar, Moon, Kyungsun, Moore, Joel, Motta, Mario, Na, Young-Hye, Nam, Yunseong, Narang, Prineha, Ohnishi, Yu-ya, Ottaviani, Daniele, Otten, Matthew, Pakin, Scott, Pascuzzi, Vincent R., Penault, Ed, Piontek, Tomasz, Pitera, Jed, Rall, Patrick, Ravi, Gokul Subramanian, Robertson, Niall, Rossi, Matteo, Rydlichowski, Piotr, Ryu, Hoon, Samsonidze, Georgy, Sato, Mitsuhisa, Saurabh, Nishant, Sharma, Vidushi, Sharma, Kunal, Shin, Soyoung, Slessman, George, Steiner, Mathias, Sitdikov, Iskandar, Suh, In-Saeng, Switzer, Eric, Tang, Wei, Thompson, Joel, Todo, Synge, Tran, Minh, Trenev, Dimitar, Trott, Christian, Tseng, Huan-Hsin, Tureci, Esin, Valinas, David García, Vallecorsa, Sofia, Wever, Christopher, Wojciechowski, Konrad, Wu, Xiaodi, Yoo, Shinjae, Yoshioka, Nobuyuki, Yu, Victor Wen-zhe, Yunoki, Seiji, Zhuk, Sergiy, Zubarev, Dmitry

Publikováno v: Future Generation Computer Systems, Volume 160, November 2024, Pages 666-710

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of the

Externí odkaz: http://arxiv.org/abs/2312.09733