Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Gyurik, Casper"'
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
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
Autor:
Gyurik, Casper, Dunjko, Vedran
Despite significant effort, the quantum machine learning community has only demonstrated quantum learning advantages for artificial cryptography-inspired datasets when dealing with classical data. In this paper we address the challenge of finding lea
Externí odkaz:
http://arxiv.org/abs/2306.16028
Publikováno v:
Nature Communications 15, 5676 (2024)
Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models
Externí odkaz:
http://arxiv.org/abs/2306.00061
Autor:
Berry, Dominic W., Su, Yuan, Gyurik, Casper, King, Robbie, Basso, Joao, Barba, Alexander Del Toro, Rajput, Abhishek, Wiebe, Nathan, Dunjko, Vedran, Babbush, Ryan
Publikováno v:
PRX Quantum 5, 010319 (2024)
Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data
Externí odkaz:
http://arxiv.org/abs/2209.13581
Autor:
Gyurik, Casper, Dunjko, Vedran
Despite years of effort, the quantum machine learning community has only been able to show quantum learning advantages for certain contrived cryptography-inspired datasets in the case of classical data. In this note, we discuss the challenges of find
Externí odkaz:
http://arxiv.org/abs/2208.06339
Publikováno v:
Quantum 7, 1078 (2023)
Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a mac
Externí odkaz:
http://arxiv.org/abs/2203.13739
Publikováno v:
Quantum 7, 893 (2023)
Quantum machine learning (QML) models based on parameterized quantum circuits are often highlighted as candidates for quantum computing's near-term ``killer application''. However, the understanding of the empirical and generalization performance of
Externí odkaz:
http://arxiv.org/abs/2105.05566