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The hybrid approach to Quantum Supervised Machine Learning is compatible with Noisy Intermediate Scale Quantum (NISQ) devices but hardly useful. Pure quantum kernels requiring fault‐tolerant quantum computers are more promising. Examples are kernels computed by means of the Quantum Fourier Transform (QFT) and kernels defined via the calculation of ...
Massimiliano Incudini+2 more
wiley +1 more source
The conjugacy classes in the unitary, symplectic and orthogonal groups over an algebraic number field [PDF]
Teruaki Asai
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Statistical Complexity of Quantum Learning
The statistical performance of quantum learning is investigated as a function of the number of training data N$N$, and of the number of copies available for each quantum state in the training and testing data sets, respectively S$S$ and V$V$. Indeed, the biggest difference in quantum learning comes from the destructive nature of quantum measurements ...
Leonardo Banchi+3 more
wiley +1 more source
Equivalence of norms for coefficients of unitary group representations [PDF]
Alberto Alesina
openalex +2 more sources
The Completeness of the Irreducible Unitary Representations of a Locally Compact Group
F. I. Mautner
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On groups generated by three-dimensional special unitary groups II [PDF]
Kok-Wee Phan
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Quantum‐Noise‐Driven Generative Diffusion Models
Diffusion Models (DMs) are today a very popular class of generative models for Machine Learning (ML), using a noisy dynamics to learn an unknown density probability of a finite set of samples in order to generate new synthetic data. This study proposes a method to generalize them into the quantum domain by introducing and investigating what are termed ...
Marco Parigi+2 more
wiley +1 more source
The unitary representations of the generalized Lorentz groups [PDF]
Ernest Thieleker
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A generalization of the unitary group
William R. Gordon, Marvin Marcus
openaire +3 more sources