Quantum Algorithm to Cubic Spline Interpolation
Changpeng Shao

TL;DR
This paper presents a quantum algorithm for cubic spline interpolation that leverages an improved quantum state preparation method, achieving exponential speedup over classical algorithms due to favorable problem conditions.
Contribution
It introduces an efficient quantum state preparation algorithm and applies the HHL algorithm to cubic spline interpolation, overcoming previous restrictions and enabling exponential speedup.
Findings
Quantum state preparation achieved with exponential speedup.
Application of HHL to cubic spline interpolation with small condition number.
Quantum algorithm outperforms classical methods without restrictions.
Abstract
HHL algorithm \cite{harrow} to solve linear system is a powerful and efficient quantum technique to deal with many matrix operations (such as matrix multiplication, powers and inversion). It inspires many applications in quantum machine learning \cite{biamonte, dunjko}. However, due to the restrictions of HHL algorithm itself, many quantum machine learning algorithms also share one or two restrictions. The most common restrictions include quantum state preparation, condition number and Hamiltonian simulation. In this work, we first give an efficient quantum algorithm to achieve quantum state preparation, which actually achieves an exponential speedup than the algorithms given in \cite{clader,lloyd13}. Then we provide an application of HHL algorithm in cubic spline interpolation problem. We will show that in this problem, the condition number is small, the preparation of quantum state is…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Polynomial and algebraic computation · Coding theory and cryptography
