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In this talk we review the GOLEM approach to one-loop calculations and present an automated implementation of this technique. This method is based on Feynman diagrams and an advanced reduction of one-loop tensor integrals which avoids…

Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks. However, many studies rely on toy datasets or heavy feature reduction, raising concerns about their scalability.…

Quantum Physics · Physics 2025-04-16 Federico Tiblias , Anna Schroeder , Yue Zhang , Mariami Gachechiladze , Iryna Gurevych

We compute the one-loop corrections to \tth up to order $\mathcal{O}(\epsilon^2)$ in the dimensional regularization parameter. We apply the projector method to compute polarized amplitudes, which generalize massless helicity amplitudes to…

High Energy Physics - Phenomenology · Physics 2023-12-18 Federico Buccioni , Philipp Alexander Kreer , Xiao Liu , Lorenzo Tancredi

We review progress in calculating one-loop scattering amplitudes required for next-to-leading-order corrections to QCD processes. The underlying technical developments include the spinor helicity formalism, color decompositions,…

High Energy Physics - Phenomenology · Physics 2010-11-15 Z. Bern , L. Dixon , D. A. Kosower

Previously proposed quantum algorithms for solving linear systems of equations cannot be implemented in the near term due to the required circuit depth. Here, we propose a hybrid quantum-classical algorithm, called Variational Quantum…

Quantum Physics · Physics 2023-11-23 Carlos Bravo-Prieto , Ryan LaRose , M. Cerezo , Yigit Subasi , Lukasz Cincio , Patrick J. Coles

Dimensional regularization of Euclidean momentum space integrals is a highly successful technique in renormalization of quantum field theories. While it yields a straightforward algorithmic method, with which to evaluate diagrams beyond…

Mathematical Physics · Physics 2020-09-03 Juuso Österman

We show that the discrete complex, and numerous hypercomplex, Fourier transforms defined and used so far by a number of researchers can be unified into a single framework based on a matrix exponential version of Euler's formula…

Rings and Algebras · Mathematics 2012-09-13 Stephen J. Sangwine , Todd A. Ell

In this short paper we present a linear constraint solver for the UniCalc system, an environment for reliable solution of mathematical modeling problems.

Mathematical Software · Computer Science 2007-05-23 E. Petrov , Yu. Kostov , E. Botoeva

The importance of quantum error correction in paving the way to build a practical quantum computer is no longer in doubt. This dissertation makes a threefold contribution to the mathematical theory of quantum error-correcting codes.…

Quantum Physics · Physics 2008-10-16 Pradeep Kiran Sarvepalli

Progress in the realisation of reliable large-scale quantum computers has motivated research into the design of quantum machine learning models. We present Quixer: a novel quantum transformer model which utilises the Linear Combination of…

Quantum Physics · Physics 2024-06-07 Nikhil Khatri , Gabriel Matos , Luuk Coopmans , Stephen Clark

We numerically solve the Klein-Gordon equation at second order in cosmological perturbation theory in closed form for a single scalar field, describing the method employed in detail. We use the slow-roll version of the second order source…

Cosmology and Nongalactic Astrophysics · Physics 2009-09-18 Ian Huston , Karim A. Malik

We present an improved version of our program package oneloop which -- written as a package for MAPLE -- solves one-loop Feynman integrals. The package is calculating one-, two- and three-point functions both algebraically and numerically…

High Energy Physics - Phenomenology · Physics 2009-10-30 L. Brücher , J. Franzkowski , D. Kreimer

We study the problem how to deal with tensor-type two-loop integrals in the Loop Regularization (LORE) scheme. We use the two-loop photon vacuum polarization in the massless Quantum Electrodynamics (QED) as the example to present the…

High Energy Physics - Phenomenology · Physics 2015-06-11 Da Huang , Ling-Fong Li , Yue-Liang Wu

We develop a simple recursive approach to treat reversible condensation polymerization with cyclization. Based upon a minimum set of balance equations, the law of mass action, Gaussian chain statistics, and the assumption of independent…

Soft Condensed Matter · Physics 2023-02-10 Michael Lang , Kiran Suresh Kumar

Detailed notes on the functions included in the DMRjulia library are included here. This discussion of how to program functions for a tensor network library are intended to be a supplement to the other documentation dedicated to explaining…

Quantum Physics · Physics 2021-11-30 Thomas E. Baker , Martin P. Thompson

We present a package 'MixedMultiplicity' for computing mixed multiplicities of ideals in a Noetherian ring which is either local or a standard graded algebra over a field. This enables us to find mixed volumes of convex lattice polytopes…

Commutative Algebra · Mathematics 2023-08-30 Kriti Goel , Vivek Mukundan , Sudeshna Roy , J. K. Verma

An accurate method to compute enclosures of Abelian integrals is developed. This allows for an accurate description of the phase portraits of planar polynomial systems that are perturbations of Hamiltonian systems. As an example, it is…

Dynamical Systems · Mathematics 2011-09-06 Tomas Johnson , Warwick Tucker

In this article, we study the triple-collinear limit of scattering amplitudes, focusing the discussion in processes which include at least one photon. To deal with infrared divergences we applied dimensional regularization (DREG) and we…

High Energy Physics - Phenomenology · Physics 2014-10-08 German F. R. Sborlini

The use of Lagrange multipliers in the context of quintessence/phantom scalar fields allows to constrain the behavior of the scalar field, which provides a powerful tool, not only for the reconstruction of cosmological solutions but also…

High Energy Physics - Theory · Physics 2015-05-30 Diego Sáez-Gómez

The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models. However, these libraries also come with steep…

Programming Languages · Computer Science 2022-04-11 Kensen Shi , David Bieber , Rishabh Singh