Matrix inversion polynomials for the quantum singular value transformation

TL;DR

This paper derives an analytic shortcut to the optimal polynomial for quantum matrix inversion, improving efficiency and reducing circuit depth in quantum singular value transformation applications.

Contribution

It introduces an analytic method to obtain the optimal polynomial for quantum matrix inversion, replacing resource-intensive numerical approaches and confirming optimality through comparisons.

Findings

The derived polynomial is optimal among various approaches.

It has the smallest maximum value on [-1,1], reducing circuit depth.

The method simplifies preprocessing for quantum singular value transformation.

Abstract

Quantum matrix inversion with the quantum singular value transformation (QSVT) requires a polynomial approximation to $1/x$. Several methods from the literature construct polynomials that achieve the known degree complexity $\mathcal{O}(\kappa\log(\kappa/\varepsilon))$ with condition number $\kappa$ and uniform error $\varepsilon$. However, the \emph{optimal} polynomial with lowest degree for fixed error $\varepsilon$ can only be approximated numerically with the resource-intensive Remez method, leading to impractical preprocessing runtimes. Here, we derive an analytic shortcut to the optimal polynomial. Comparisons with other polynomials from the literature, based on Taylor expansion, Chebyshev iteration, and convex optimization, confirm that our result is optimal. Furthermore, for large $\kappa\log(\kappa/\varepsilon)$, our polynomial has the smallest maximum value on $[-1,1]$ of all approaches considered, leading to reduced circuit depth due to the normalization condition of QSVT. With the Python code provided, this paper will also be useful for practitioners in the field.