Zobrazeno 1 - 10
of 758
pro vyhledávání: '"A. Klepsch"'
Autor:
BMW Group Quantum Team, Klepsch, Johannes, Finžgar, Jernej Rudi, Kiwit, Florian, Hölscher, Leonhard, Erdmann, Marvin, Müller, Lukas, Kumar, Chandan, Luckow, Andre
Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engineering, being particularly promising. This chapter investigates state-
Externí odkaz:
http://arxiv.org/abs/2409.14183
Autor:
Hölscher, Leonhard, Rao, Pooja, Müller, Lukas, Klepsch, Johannes, Luckow, Andre, Stollenwerk, Tobias, Wilhelm, Frank K.
Tensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapted recently for solving the Navier-Stokes equations, which describe a
Externí odkaz:
http://arxiv.org/abs/2406.17823
Autor:
Mirza, Adrian, Alampara, Nawaf, Kunchapu, Sreekanth, Ríos-García, Martiño, Emoekabu, Benedict, Krishnan, Aswanth, Gupta, Tanya, Schilling-Wilhelmi, Mara, Okereke, Macjonathan, Aneesh, Anagha, Elahi, Amir Mohammad, Asgari, Mehrdad, Eberhardt, Juliane, Elbeheiry, Hani M., Gil, María Victoria, Greiner, Maximilian, Holick, Caroline T., Glaubitz, Christina, Hoffmann, Tim, Ibrahim, Abdelrahman, Klepsch, Lea C., Köster, Yannik, Kreth, Fabian Alexander, Meyer, Jakob, Miret, Santiago, Peschel, Jan Matthias, Ringleb, Michael, Roesner, Nicole, Schreiber, Johanna, Schubert, Ulrich S., Stafast, Leanne M., Wonanke, Dinga, Pieler, Michael, Schwaller, Philippe, Jablonka, Kevin Maik
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical
Externí odkaz:
http://arxiv.org/abs/2404.01475
Autor:
Kiwit, Florian J., Marso, Marwa, Ross, Philipp, Riofrío, Carlos A., Klepsch, Johannes, Luckow, Andre
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection. The QUantum computing Appli
Externí odkaz:
http://arxiv.org/abs/2308.04082
Autor:
Wintersperger, Karen, Dommert, Florian, Ehmer, Thomas, Hoursanov, Andrey, Klepsch, Johannes, Mauerer, Wolfgang, Reuber, Georg, Strohm, Thomas, Yin, Ming, Luber, Sebastian
Publikováno v:
EPJ Quantum Technology 10, 32 (2023)
We present an industrial end-user perspective on the current state of quantum computing hardware for one specific technological approach, the neutral atom platform. Our aim is to assist developers in understanding the impact of the specific propertie
Externí odkaz:
http://arxiv.org/abs/2304.14360
Autor:
Riofrío, Carlos A., Mitevski, Oliver, Jones, Caitlin, Krellner, Florian, Vučković, Aleksandar, Doetsch, Joseph, Klepsch, Johannes, Ehmer, Thomas, Luckow, Andre
Publikováno v:
ACM Transactions on Quantum Computing, Volume 5, Issue 2 (2024)
Quantum generative modeling is a growing area of interest for industry-relevant applications. With the field still in its infancy, there are many competing techniques. This work is an attempt to systematically compare a broad range of these technique
Externí odkaz:
http://arxiv.org/abs/2301.09363
Autor:
Awasthi, Abhishek, Bär, Francesco, Doetsch, Joseph, Ehm, Hans, Erdmann, Marvin, Hess, Maximilian, Klepsch, Johannes, Limacher, Peter A., Luckow, Andre, Niedermeier, Christoph, Palackal, Lilly, Pfeiffer, Ruben, Ross, Philipp, Safi, Hila, Schönmeier-Kromer, Janik, von Sicard, Oliver, Wenger, Yannick, Wintersperger, Karen, Yarkoni, Sheir
Publikováno v:
Arai, K. (eds) Intelligent Computing. SAI 2023. Lecture Notes in Networks and Systems, vol 739
Optimization problems are ubiquitous in various industrial settings, and multi-knapsack optimization is one recurrent task faced daily by several industries. The advent of quantum computing has opened a new paradigm for computationally intensive task
Externí odkaz:
http://arxiv.org/abs/2301.05750
Autor:
Guala, Diego, Zhang, Shaoming, Cruz, Esther, Riofrío, Carlos A., Klepsch, Johannes, Arrazola, Juan Miguel
Circuit design for quantum machine learning remains a formidable challenge. Inspired by the applications of tensor networks across different fields and their novel presence in the classical machine learning context, one proposed method to design vari
Externí odkaz:
http://arxiv.org/abs/2209.11058
Autor:
Garrido, Gonzalo Munilla, Schmidt, Kaja, Harth-Kitzerow, Christopher, Klepsch, Johannes, Luckow, Andre, Matthes, Florian
Publikováno v:
2021 IEEE International Conference on Big Data (Big Data)
Privacy-enhancing technologies (PETs) are becoming increasingly crucial for addressing customer needs, security, privacy (e.g., enhancing anonymity and confidentiality), and regulatory requirements. However, applying PETs in organizations requires a
Externí odkaz:
http://arxiv.org/abs/2209.05085
Autor:
Schuetz, Martin J. A., Brubaker, J. Kyle, Montagu, Henry, van Dijk, Yannick, Klepsch, Johannes, Ross, Philipp, Luckow, Andre, Resende, Mauricio G. C., Katzgraber, Helmut G.
Publikováno v:
Phys. Rev. Applied 18, 054045 (2022)
We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well as path relinking for solution refinement. The cor
Externí odkaz:
http://arxiv.org/abs/2206.03651