Matrix inversion polynomials for the quantum singular value transformation

• URL: http://arxiv.org/abs/2507.15537v1
• Date: Mon, 21 Jul 2025 12:04:56 GMT
• Title: Matrix inversion polynomials for the quantum singular value transformation
• Authors: Christoph Sünderhauf, Zalán Németh, Adnaan Walayat, Andrew Patterson, Bjorn K. Berntson,
• Abstract summary: A quantum inversion matrix with the quantum singular value transformation (QSVT) requires an approximation to $1/x$.<n>Several methods from the literature achieve the known degree complexity $mathcalO(kappalog(kappa/varepsilon))$ with condition number $kappa$ and uniform error $varepsilon$.<n>Here, we derive an analytic shortcut to the optimal, based on Taylor expansion, Chebyshev and convex optimization.
• Score: 3.718165623644259
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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