Search Results for author:
Found 9 papers, 1 papers with code
Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer
In the compression algorithm, we define a mapping from lattice QCD data of floating-point numbers to the binary coefficients that closely reconstruct the input data from a set of basis vectors.
Lossy compression of statistical data using quantum annealer
We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables.
Precision Nucleon Charges and Form Factors Using 2+1-flavor Lattice QCD
Similarly, we find evidence that the $N\pi\pi $ excited state contributes to the correlation functions with the vector current, consistent with the vector meson dominance model.
High Energy Physics - Lattice High Energy Physics - Phenomenology
Contribution of the QCD $Θ$-term to nucleon electric dipole moment
Using the excited state spectrum from fits to the two-point function, we find $d_n^\Theta$ is small, $|d_n^\Theta| \lesssim 0. 01 \overline \Theta e$ fm, whereas for the proton we get $|d_p^\Theta| \sim 0. 02 \overline \Theta e$ fm.
High Energy Physics - Lattice High Energy Physics - Phenomenology
Nucleon Momentum Fraction, Helicity and Transversity from 2+1-flavor Lattice QCD
Our final results, in the $\overline{\rm MS}$ scheme at 2 GeV, are $\langle x \rangle_{u-d} = 0. 160(16)(20)$, $\langle x \rangle_{\Delta u-\Delta d} = 0. 192(13)(20)$ and $\langle x \rangle_{\delta u-\delta d} = 0. 215(17)(20)$, where the first error is the overall analysis uncertainty assuming excited-state contributions have been removed, and the second is an additional systematic uncertainty due to possible residual excited-state contributions.
High Energy Physics - Lattice
A machine learning approach for efficient multi-dimensional integration
Then, the difference between the approximation and the true answer is calculated to correct the bias in the approximation of the integral induced by a ML prediction error.
A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer
We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer.
Quantum Algorithm Implementations for Beginners
As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers.
Emerging Technologies Quantum Physics
Estimation of matrix trace using machine learning
We present a new trace estimator of the matrix whose explicit form is not given but its matrix multiplication to a vector is available.
