Quantum Algorithm For Solving Nonlinear Algebraic Equations

• URL: http://arxiv.org/abs/2404.03810v2
• Date: Thu, 1 Aug 2024 14:16:05 GMT
• Title: Quantum Algorithm For Solving Nonlinear Algebraic Equations
• Authors: Nhat A. Nghiem, Tzu-Chieh Wei,
• Abstract summary: We give a quantum algorithm for solving a system of nonlinear algebraic equations. A detailed analysis are carried out to reveal that our method polylogarithmic time in relative to the number of variables. In particular, we show that our method can be modified with little effort to deal with various types, thus implying the generality of our approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nonlinear equations are challenging to solve due to their inherently nonlinear nature. As analytical solutions typically do not exist, numerical methods have been developed to tackle their solutions. In this article, we give a quantum algorithm for solving a system of nonlinear algebraic equations, in which each equation is a multivariate polynomial of known coefficients. Building upon the classical Newton method and some recent works on quantum algorithm plus block encoding from the quantum singular value transformation, we show how to invert the Jacobian matrix to execute Newton's iterative method for solving nonlinear equations, where each contributing equation is a homogeneous polynomial of an even degree. A detailed analysis are then carried out to reveal that our method achieves polylogarithmic time in relative to the number of variables. Furthermore, the number of required qubits is logarithmic in the number of variables. In particular, we also show that our method can be modified with little effort to deal with polynomial of various types, thus implying the generality of our approach. Some examples coming from physics and algebraic geometry, such as Gross-Pitaevski equation, Lotka-Volterra equations, and intersection of algebraic varieties, involving nonlinear partial differential equations are provided to motivate the potential application, with a description on how to extend our algorithm with even less effort in such a scenario. Our work thus marks a further important step towards quantum advantage in nonlinear science, enabled by the framework of quantum singular value transformation.

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