Results 151 to 160 of about 163,902 (301)
The Stabilization of Two-Dimensional Recursive Filters via the Discrete Hilbert Transform
, 1973 Randol R. Read, Sven Treitel
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The noncommutative Hilbert transform approach to free entropy [PDF]
, 1998 Dan Voiculescu
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Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning
Advanced Quantum Technologies, EarlyView.The use of QML in the realm of nuclear physics at low energy is almost nonexistent. Three examples of the use of quantum computing and quantum machine in nuclear physics are presented: the determination of the phase/shape in nuclear models, the calculation of the ground state energy, and the identification of particles in nuclear physics experiments ...
José‐Enrique García‐Ramos +4 more
wiley +1 more source
Advanced Quantum Technologies, EarlyView.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
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Statistical Complexity of Quantum Learning
Advanced Quantum Technologies, EarlyView.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
A REMARK ON THE ARCSINE DISTRIBUTION AND THE HILBERT TRANSFORM. [PDF]
J Fourier Anal Appl, 2019 Coifman RR, Steinerberger S.
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A sharp estimate for the weighted Hilbert transform via Bellman functions [PDF]
, 2002 Stefanie Petermichl, Janine Wittwer
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Quantum‐Noise‐Driven Generative Diffusion Models
Advanced Quantum Technologies, EarlyView.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