Results 31 to 40 of about 16,720 (151)

WAPS-Quant: Low-Bit Post-Training Quantization Using Weight-Activation Product Scaling

open access: yesIEEE Access
Post-Training Quantization (PTQ) has been effectively compressing neural networks into very few bits using a limited calibration dataset. Various quantization methods utilizing second-order error have been proposed and demonstrated good performance ...
Geunjae Choi, Kamin Lee, Nojun Kwak
doaj   +1 more source

Survey of Post-Training Quantization Methods (Invited) [PDF]

open access: yesJisuanji gongcheng
Post-Training Quantization (PTQ) is an efficient model compression method that converts the parameters of high-precision floating-point models into low-bit integer representations without requiring retraining, using only a small amount of unlabeled ...
ZHANG Junna, WANG Hongzun, DING Chuntao
doaj   +1 more source

Closed-Form Sum-Rate Analysis of Interference Alignment with Limited Feedback Based on Scalar Quantization and Random Vector Quantization

open access: yesApplied Sciences, 2022
Interference alignment (IA) is a promising interference management technique to achieve the theoretical optimal degree of freedom (DoF) performance in multi-user cooperation scenarios.
Long Suo, Fei Liu
doaj   +1 more source

A Two-Phase Super-Resolution Quantization Scheme Optimized by Sequential Grading and Data Bias Correction [PDF]

open access: yesJisuanji gongcheng
Model quantization technology effectively reduces model storage and computational overhead by mapping high-precision floating-point data to low-bit discrete spaces.
HAO Liang, SU Bohejun, WANG Jinghua, XU Yong
doaj   +1 more source

Canonical quantization of cylindrical waveguides: a gauge-based approach

open access: yesJournal of Physics Communications
We present a canonical quantization of electromagnetic modes in cylindrical waveguides, extending a gauge-based formalism previously developed for Cartesian geometries (Collin and Delattre 2025 New J. Phys. 27 093502).
Alexandre Delattre, Eddy Collin
doaj   +1 more source

Exponentially more precise quantum simulation of fermions in second quantization

open access: yesNew Journal of Physics, 2016
We introduce novel algorithms for the quantum simulation of fermionic systems which are dramatically more efficient than those based on the Lie–Trotter–Suzuki decomposition.
Ryan Babbush   +5 more
doaj   +1 more source

Quantum Cosmology of Fab Four John Theory with Conformable Fractional Derivative

open access: yesUniverse, 2020
We study a quantization via fractional derivative of a nonminimal derivative coupling cosmological theory, namely, the Fab Four John theory. Its Hamiltonian version presents the issue of fractional powers in the momenta.
Isaac Torres   +3 more
doaj   +1 more source

Second-Order Conformally Equivariant Quantization in Dimension 1|2

open access: yesSymmetry, Integrability and Geometry: Methods and Applications, 2009
This paper is the next step of an ambitious program to develop conformally equivariant quantization on supermanifolds. This problem was considered so far in (super)dimensions 1 and 1|1.
Najla Mellouli
doaj   +1 more source

Extension of explicit formulas in Poissonian white noise analysis using harmonic analysis on configuration spaces

open access: yesCondensed Matter Physics, 2008
Harmonic analysis on configuration spaces is used in order to extend explicit expressions for the images of creation, annihilation, and second quantization operators in L2-spaces with respect to Poisson point processes to a set of functions larger than ...
Yu.G.Kondratiev, T.Kuna, M.J.Oliveira
doaj   +1 more source

Quantum simulations of chemistry in first quantization with any basis set

open access: yesnpj Quantum Information
Quantum computation of the energy of molecules and materials is one of the most promising applications of fault-tolerant quantum computers. Practical applications require development of quantum algorithms with reduced resource requirements. Previous work
Timothy N. Georges   +5 more
doaj   +1 more source

Home - About - Disclaimer - Privacy