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Linear equations play a pivotal role in many areas of science and engineering, making efficient solutions to linear systems highly desirable. The development of quantum algorithms for solving linear systems has been a significant…

Quantum Physics · Physics 2025-02-20 Nhat A. Nghiem

This paper presents a novel framework for high-dimensional nonlinear quantum computation that exploits tensor products of amplified vector and matrix encodings to efficiently evaluate multivariate polynomials. The approach enables the…

Quantum Physics · Physics 2025-10-01 Matthias Deiml , Daniel Peterseim

We apply preconditioning, which is widely used in classical solvers for linear systems $A\textbf{x}=\textbf{b}$, to the variational quantum linear solver. By utilizing incomplete LU factorization as a preconditioner for linear equations…

Solving systems of linear equations is one of the most important primitives in quantum computing that has the potential to provide a practical quantum advantage in many different areas, including in optimization, simulation, and machine…

Quantum Physics · Physics 2021-09-10 Sander Gribling , Iordanis Kerenidis , Dániel Szilágyi

The computational requirements of future large scale radio telescopes are expected to scale well beyond the capabilities of conventional digital resources. Current and planned telescopes are generally limited in their scientific potential…

Instrumentation and Methods for Astrophysics · Physics 2023-10-19 Nicolas Renaud , Pablo Rodríguez-Sánchez , Johan Hidding , P. Chris Broekema

We introduce a novel quantum algorithm for the lattice Boltzmann method (LBM) based on the one-step simplified LBM. The structure of the algorithm allows for more flexibility in modelling different physics in contrast to earlier quantum…

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of…

Superconducting quantum circuits are promising candidate for building scalable quantum computers. Here, we use a four-qubit superconducting quantum processor to solve a two-dimensional system of linear equations based on a quantum algorithm…

Simulating nonlinear partial differential equations (PDEs) such as the Navier--Stokes (NS) equations remains computationally intensive, especially when implicit time integration is used to capture multiscale flow dynamics. This work…

Fluid Dynamics · Physics 2025-08-29 Shaobo Yao , Zhiyu Duan , Ziteng Wang , Wenwen Zhao , Shiying Xiong

Variational quantum algorithms (VQAs) are promising hybrid quantum-classical methods designed to leverage the computational advantages of quantum computing while mitigating the limitations of current noisy intermediate-scale quantum (NISQ)…

Computational Engineering, Finance, and Science · Computer Science 2025-04-18 Saibal De , Oliver Knitter , Rohan Kodati , Paramsothy Jayakumar , James Stokes , Shravan Veerapaneni

The Vlasov-Maxwell equations provide an \textit{ab-initio} description of collisionless plasmas, but solving them is often impractical because of the wide range of spatial and temporal scales that must be resolved and the high…

Computational Physics · Physics 2026-05-14 Erika Ye , Nuno Loureiro

The present paper gives a review of our recent progress and latest results for novel linear-algebraic algorithms and its application to large-scale quantum material simulations or electronic structure calculations. The algorithms are…

We present an efficient quantum algorithm to simulate nonlinear differential equations with polynomial vector fields of arbitrary degree on quantum platforms. Models of physical systems that are governed by ordinary differential equations…

Dynamical Systems · Mathematics 2023-02-08 Amit Surana , Abeynaya Gnanasekaran , Tuhin Sahai

Solving linear systems of equations is a fundamental problem with a wide variety of applications across many fields of science, and there is increasing effort to develop quantum linear solver algorithms. [Suba\c{s}i et al., Phys. Rev. Lett.…

Quantum Physics · Physics 2026-01-09 David Jennings , Matteo Lostaglio , Sam Pallister , Andrew T Sornborger , Yiğit Subaşı

We present a systematic pathway for solving differential equations within the quantum linear systems framework by combining block encoding with Quantum Singular Value Transformation (QSVT). The approach is demonstrated on a complex…

Quantum Physics · Physics 2026-05-12 Abhishek Setty

Time-dependent linear differential equations are a common type of problem that needs to be solved in classical physics. Here we provide a quantum algorithm for solving time-dependent linear differential equations with logarithmic dependence…

Quantum Physics · Physics 2024-06-19 Dominic W. Berry , Pedro C. S. Costa

In this paper, we present very first results for the adaptive solution on a grid of the phase space of the Vlasov equation arising in particles accelarator and plasma physics. The numerical algorithm is based on a semi-Lagrangian method…

Numerical Analysis · Mathematics 2007-05-23 Nicolas Besse , Francis Filbet , Michael Gutnic , Ioana Paun , Eric Sonnendrücker

A method for solving linearized Vlasov equation for low-frequency, long-wavelength electromagnetic modes in magnetically confined inhomogeneous plasmas is described. The relevant non-local solution that includes the lowest-significant-order…

Plasma Physics · Physics 2025-12-09 Bamandas Basu

In contexts where relevant problems can easily attain configuration spaces of enormous sizes, solving Linear Differential Equations (LDEs) can become a hard achievement for classical computers; on the other hand, the rise of quantum…

Quantum Physics · Physics 2023-01-31 João H. Romeiro , Frederico Brito

This work presents a quantum algorithm for solving linear systems of equations of the form $\mathbf{A}{\frac{\mathbf{\partial f}}{\mathbf{\partial x}}} = \mathbf{B}\mathbf{f}$, based on the Quantum Singular Value Transformation (QSVT). The…

Quantum Physics · Physics 2025-07-18 Gal G. Shaviner , Ziv Chen , Steven H. Frankel