An Optimal Quantum Linear Systems Algorithm
Abstract
In the quantum linear systems problem (QLSP), we are given query access to a $d$-sparse $N\times N$ matrix $A$ with condition number $κ$, and the ability to prepare a quantum state proportional to a vector $\vec b$. The goal is to output an $ε$-approximation to the quantum state proportional to the solution $\vec x$ of $A\vec{x}=\vec{b}$. Following a long line of work, the best previously known quantum algorithms for the QLSP had query complexities $O(κd\log(1/ε))$ and $κ\sqrt d(κd/ε)^{o(1)}$, w...
Description / Details
In the quantum linear systems problem (QLSP), we are given query access to a -sparse matrix with condition number , and the ability to prepare a quantum state proportional to a vector . The goal is to output an -approximation to the quantum state proportional to the solution of . Following a long line of work, the best previously known quantum algorithms for the QLSP had query complexities and , while the best known lower bounds were and . We improve these bounds and show that the complexity of the QLSP is . We also resolve an open problem of Berry and Childs by showing that any unitary can be implemented with bounded error using queries to its matrix entries.
Source: arXiv:2609.35660v1 - http://arxiv.org/abs/2609.35660v1 PDF: https://arxiv.org/pdf/2609.35660v1 Original Link: http://arxiv.org/abs/2609.35660v1
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Sep 29, 2026
Quantum Computing
Quantum Physics
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