Estimating QSVT angles for matrix inversion with large condition numbers

Quantum Singular Value Transformation (QSVT) is a state-of-the-art, near-optimal quantum algorithm that can be used for matrix inversion. The QSVT circuit is parameterized by a sequence of angles that must be pre-calculated classically, with the number of angles increasing as the matrix condition number grows. Computing QSVT angles for ill-conditioned problems is a numerically challenging task. We propose a numerical technique for estimating QSVT angles for large condition numbers. This technique allows one to avoid expensive numerical computations of QSVT angles and to emulate QSVT circuits for solving ill-conditioned problems.

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Publication Details

Journal
Journal of Computational Physics
Published
2025-01-22
DOI
https://doi.org/10.1016/j.jcp.2025.113767
Citations
1
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
2.48

Funders

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article

Estimating QSVT angles for matrix inversion with large condition numbers

I. Novikau, I. Joseph
1 citations
Journal of Computational Physics
Quantum Computing Algorithms and Architecture
2.48
article

Estimating QSVT angles for matrix inversion with large condition numbers

I. Novikau, I. Joseph
article en
1 citations

Abstract

Quantum Singular Value Transformation (QSVT) is a state-of-the-art, near-optimal quantum algorithm that can be used for matrix inversion. The QSVT circuit is parameterized by a sequence of angles that must be pre-calculated classically, with the number of angles increasing as the matrix condition number grows. Computing QSVT angles for ill-conditioned problems is a numerically challenging task. We propose a numerical technique for estimating QSVT angles for large condition numbers. This technique allows one to avoid expensive numerical computations of QSVT angles and to emulate QSVT circuits for solving ill-conditioned problems.

Journal of Computational PhysicsVol. 525
Lawrence Livermore National Laboratory (US)
U.S. Department of Energy
Openalex Percentile: Top 12%
Quantum Computing Algorithms and Architecture
2.48
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