Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Riofrío, Carlos A."'
Autor:
Quetschlich, Nils, Kiwit, Florian J., Wolf, Maximilian A., Riofrio, Carlos A., Burgholzer, Lukas, Luckow, Andre, Wille, Robert
Quantum computing has made tremendous improvements in both software and hardware that have sparked interest in academia and industry to realize quantum computing applications. To this end, several steps are necessary: The underlying problem must be e
Externí odkaz:
http://arxiv.org/abs/2404.12433
Autor:
Kiwit, Florian J., Wolf, Maximilian A., Marso, Marwa, Ross, Philipp, Lorenz, Jeanette M., Riofrío, Carlos A., Luckow, Andre
Quantum computing promises a disruptive impact on machine learning algorithms, taking advantage of the exponentially large Hilbert space available. However, it is not clear how to scale quantum machine learning (QML) to industrial-level applications.
Externí odkaz:
http://arxiv.org/abs/2403.18662
Autor:
Pfeiffer, Frederik, Werninghaus, Max, Schweizer, Christian, Bruckmoser, Niklas, Koch, Leon, Glaser, Niklas J., Huber, Gerhard, Bunch, David, Haslbeck, Franz X., Knudsen, M., Krylov, Gleb, Liegener, Klaus, Marx, Achim, Richard, Lea, Romeiro, João H., Roy, Federico, Schirk, Johannes, Schneider, Christian, Singh, Malay, Södergren, Lasse, Tsitsilin, Ivan, Wallner, Florian, Riofrío, Carlos A., Filipp, Stefan
To control and measure the state of a quantum system it must necessarily be coupled to external degrees of freedom. This inevitably leads to spontaneous emission via the Purcell effect, photon-induced dephasing from measurement back-action, and error
Externí odkaz:
http://arxiv.org/abs/2312.16988
Machine learning tasks are an exciting application for quantum computers, as it has been proven that they can learn certain problems more efficiently than classical ones. Applying quantum machine learning algorithms to classical data can have many im
Externí odkaz:
http://arxiv.org/abs/2311.07666
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:
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:
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
Existing convergence guarantees for the mirror descent algorithm require the objective function to have a bounded gradient or be smooth relative to a Legendre function. The bounded gradient and relative smoothness conditions, however, may not hold in
Externí odkaz:
http://arxiv.org/abs/1805.12232
Publikováno v:
Phys. Rev. Lett. 121, 130404 (2018)
The spin-coherent-state positive-operator-valued-measure (POVM) is a fundamental measurement in quantum science, with applications including tomography, metrology, teleportation, benchmarking, and measurement of Husimi phase space probabilities. We p
Externí odkaz:
http://arxiv.org/abs/1805.01012
Autor:
Gallegos-Riofrío, Carlos Andres, Arab, Hassan, Carrasco-Torrontegui, Amaya, Gould, Rachelle K.
Publikováno v:
In Current Research in Environmental Sustainability 2022 4