Results 171 to 180 of about 4,977,477 (349)
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
On Semi-Groups of Selfadjoint Transformations in Hilbert Space [PDF]
, 1938 Béla de Sz. Nagy
openalex +1 more source
Linear Transformations Between Hilbert Spaces and the Application of This Theory to Linear Partial Differential Equations [PDF]
, 1935 Francis J. Murray
openalex +1 more source
Continuous Function Spaces Isometric to Hilbert Space [PDF]
, 1957 William F. Donoghue
openalex +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
wiley +1 more source
Frameness bound for frame of subspaces
Sahand Communications in Mathematical Analysis, 2014 In this paper, we show that in each nite dimensional Hilbert space, a frame of subspaces is an ultra Bessel sequence of subspaces. We also show that every frame of subspaces in a nite dimensional Hilbert space has frameness bound.
M. R. Abdollahpour, A. Shekari
doaj
The Hilbert Transform on Rearrangement-Invariant Spaces [PDF]
, 1967 David W. Boyd
openalex +1 more source
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