Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Dunjko, Vedran"'
Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the problem is known to be multi-level as the performance of a given architectu
Externí odkaz:
http://arxiv.org/abs/2407.20091
The quest for successful variational quantum machine learning (QML) relies on the design of suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical machine learning. Successful QML models must fulfill the propertie
Externí odkaz:
http://arxiv.org/abs/2406.07072
Advice classes in computational complexity have frequently been used to model real-world scenarios encountered in cryptography, quantum computing and machine learning, where some computational task may be broken down into a preprocessing and deployme
Externí odkaz:
http://arxiv.org/abs/2405.18155
Quantum computers are believed to bring computational advantages in simulating quantum many body systems. However, recent works have shown that classical machine learning algorithms are able to predict numerous properties of quantum systems with clas
Externí odkaz:
http://arxiv.org/abs/2405.02027
Parameterized quantum circuits have been extensively used as the basis for machine learning models in regression, classification, and generative tasks. For supervised learning, their expressivity has been thoroughly investigated and several universal
Externí odkaz:
http://arxiv.org/abs/2402.09848
Autor:
Patel, Yash J., Kundu, Akash, Ostaszewski, Mateusz, Bonet-Monroig, Xavier, Dunjko, Vedran, Danaci, Onur
The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational quantum algorithms (VQAs) offer a potential solution by fixing the circuit architecture and optimizing in
Externí odkaz:
http://arxiv.org/abs/2402.03500
Autor:
Abbas, Amira, Ambainis, Andris, Augustino, Brandon, Bärtschi, Andreas, Buhrman, Harry, Coffrin, Carleton, Cortiana, Giorgio, Dunjko, Vedran, Egger, Daniel J., Elmegreen, Bruce G., Franco, Nicola, Fratini, Filippo, Fuller, Bryce, Gacon, Julien, Gonciulea, Constantin, Gribling, Sander, Gupta, Swati, Hadfield, Stuart, Heese, Raoul, Kircher, Gerhard, Kleinert, Thomas, Koch, Thorsten, Korpas, Georgios, Lenk, Steve, Marecek, Jakub, Markov, Vanio, Mazzola, Guglielmo, Mensa, Stefano, Mohseni, Naeimeh, Nannicini, Giacomo, O'Meara, Corey, Tapia, Elena Peña, Pokutta, Sebastian, Proissl, Manuel, Rebentrost, Patrick, Sahin, Emre, Symons, Benjamin C. B., Tornow, Sabine, Valls, Victor, Woerner, Stefan, Wolf-Bauwens, Mira L., Yard, Jon, Yarkoni, Sheir, Zechiel, Dirk, Zhuk, Sergiy, Zoufal, Christa
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum algorithms has developed in many areas, with optimization being one of
Externí odkaz:
http://arxiv.org/abs/2312.02279
In recent works, much progress has been made with regards to so-called randomized measurement strategies, which include the famous methods of classical shadows and shadow tomography. In such strategies, unknown quantum states are first measured (or `
Externí odkaz:
http://arxiv.org/abs/2311.12618
Hamiltonian simulation is believed to be one of the first tasks where quantum computers can yield a quantum advantage. One of the most popular methods of Hamiltonian simulation is Trotterization, which makes use of the approximation $e^{i\sum_jA_j}\s
Externí odkaz:
http://arxiv.org/abs/2311.04285
Publikováno v:
Machine Learning: Science and Technology 5, 025003 (2024)
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are inner products of feature vectors living in large feature spaces. Quan
Externí odkaz:
http://arxiv.org/abs/2309.14419