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Eigenvector continuation is a computational method that finds the extremal eigenvalues and eigenvectors of a Hamiltonian matrix with one or more control parameters. It does this by projection onto a subspace of eigenvectors corresponding to…

Nuclear Theory · Physics 2021-01-22 Avik Sarkar , Dean Lee

A common challenge faced in quantum physics is finding the extremal eigenvalues and eigenvectors of a Hamiltonian matrix in a vector space so large that linear algebra operations on general vectors are not possible. There are numerous…

Nuclear Theory · Physics 2018-07-18 Dillon Frame , Rongzheng He , Ilse Ipsen , Daniel Lee , Dean Lee , Ermal Rrapaj

We develop an extension of eigenvector continuation (EC) that makes it possible to extrapolate simulations of quantum systems in finite periodic boxes across large ranges of box sizes. The formal justification for this approach, which we…

Nuclear Theory · Physics 2022-07-19 Nuwan Yapa , Sebastian König

A typical task for classical and quantum computing in chemistry is finding a potential energy surface (PES) along a reaction coordinate, which involves solving the quantum chemistry problem for many points along the reaction path.…

Chemical Physics · Physics 2023-11-16 Carlos Mejuto-Zaera , Alexander F. Kemper

Emulators that can bypass computationally expensive scientific calculations with high accuracy and speed can enable new studies of fundamental science as well as more potential applications. In this work we discuss solving a system of…

Nuclear Theory · Physics 2022-07-06 Avik Sarkar , Dean Lee

The study of open quantum systems (OQSs), i.e., systems interacting with an environment, impacts our understanding of exotic nuclei in low-energy nuclear physics, hadrons, cold-atom systems, or even noisy intermediate-scale quantum…

Nuclear Theory · Physics 2023-06-28 Nuwan Yapa , Kévin Fossez , Sebastian König

Quantum subspace diagonalization (QSD) methods are quantum-classical hybrid methods, commonly used to find ground and excited state energies by projecting the Hamiltonian to a smaller subspace. In applying these, the choice of subspace…

Quantum Physics · Physics 2022-09-23 Akhil Francis , Anjali A. Agrawal , Jack H. Howard , Efekan Kökcü , A. F. Kemper

In this article, we study eigenvalue problems associated to self-adjoint operators and their approximation obtained by subspace projection, as used in the reduced basis method for instance. We provide error bounds between the exact…

Mathematical Physics · Physics 2025-06-16 Louis Garrigue , Benjamin Stamm

Shell-model calculations play a key role in elucidating various properties of nuclei. In general, those studies require a huge number of calculations to be repeated for parameter calibration and quantifying uncertainties. To reduce the…

Nuclear Theory · Physics 2022-06-01 Sota Yoshida , Noritaka Shimizu

We present a new numerical technique to solve large-scale eigenvalue problems. It is based on the projection technique, used in strongly correlated quantum many-body systems, where first an effective approximate model of smaller complexity…

Strongly Correlated Electrons · Physics 2015-05-19 Ralf Gamillscheg , Gundolf Haase , Wolfgang von der Linden

Subspace methods are powerful, noise-resilient methods that can effectively prepare ground states on quantum computers. The challenge is to get a subspace with a small condition number that spans the states of interest using minimal quantum…

Quantum Physics · Physics 2025-05-07 Anjali A. Agrawal , João C. Getelina , Akhil Francis , A. F. Kemper

We present the reduced basis method as a tool for developing emulators for equations with tunable parameters within the context of the nuclear many-body problem. The method uses a basis expansion informed by a set of solutions for a few…

Nuclear Theory · Physics 2022-11-30 Edgard Bonilla , Pablo Giuliani , Kyle Godbey , Dean Lee

The development of emulators for the evaluation of many-body observables has gained increasing attention over the last years. In particular the framework of eigenvector continuation (EC) has been identified as a powerful tool when the…

Nuclear Theory · Physics 2024-02-16 Margarida Companys Franzke , Alexander Tichai , Kai Hebeler , Achim Schwenk

The method of computing eigenvectors from eigenvalues of submatrices can be shown as equivalent to a method of computing the constraint which achieves specified stationary values of a quadratic optimization. Similarly, we show computation…

Rings and Algebras · Mathematics 2019-12-10 John Lakness

Quantum many-body theory has witnessed tremendous progress in various fields, ranging from atomic and solid-state physics to quantum chemistry and nuclear structure. Due to the inherent computational burden linked to the ab initio treatment…

Recently, a new eigenvalue problem, called the transmission eigenvalue problem, has attracted many researchers. The problem arose in inverse scattering theory for inhomogeneous media and has important applications in a variety of inverse…

Numerical Analysis · Mathematics 2016-11-23 Ruihao Huang , Allan A. Struthers , Jiguang Sun , Ruming Zhang

Eigensolvers involving complex moments can determine all the eigenvalues in a given region in the complex plane and the corresponding eigenvectors of a regular linear matrix pencil. The complex moment acts as a filter for extracting…

Numerical Analysis · Mathematics 2021-09-22 Keiichi Morikuni

The problem of construction of projection operators on eigen-subspaces of symmetry operators is considered. This problem arises in many approximate methods for solving time-independent and time-dependent quantum problems, and its solution…

Quantum Physics · Physics 2019-10-08 Artur F. Izmaylov

We develop a class of emulators for solving quantum three-body scattering problems. They are based on combining the variational method for scattering observables and the recently proposed eigenvector continuation concept. The emulators are…

Nuclear Theory · Physics 2022-07-13 Xilin Zhang , R. J. Furnstahl

Large-scale eigenvalue problems arise in various fields of science and engineering and demand computationally efficient solutions. In this study, we investigate the subspace approximation for parametric linear eigenvalue problems, aiming to…

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