5 Result(s)
within
M. Cerezo
Science, Humanities and Social Sciences, multidisciplinary
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Article
Open AccessExponential concentration in quantum kernel methods
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when ...
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Article
Open AccessQuantum algorithms for geologic fracture networks
Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglec...
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Article
Open AccessGeneralization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generaliz...
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Article
Open AccessNoise-induced barren plateaus in variational quantum algorithms
Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise on NISQ devices places fundamental limitations...
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Article
Open AccessCost function dependent barren plateaus in shallow parametrized quantum circuits
Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they ...
