A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm
Keywords: quantum linear system problem, proximal point algorithm, quantum algorithm, catalyst
Abstract: Solving systems of linear equations is a fundamental problem, but it can be computationally intensive for classical algorithms in high dimensions. Existing quantum algorithms can achieve exponential speedups for the quantum linear system problem (QLSP) in terms of the problem dimension, but even such a theoretical advantage is bottlenecked by the condition number of the coefficient matrix. In this work, we propose a new quantum algorithm for QLSP inspired by the classical proximal point algorithm (PPA). Our proposed method can be viewed as a meta-algorithm that allows inverting a modified matrix via an existing \texttt{QLSP\_solver}, thereby directly approximating the solution vector instead of approximating the inverse of the coefficient matrix. By carefully choosing the step size $\eta$, the proposed algorithm can effectively precondition the linear system to mitigate the dependence on condition numbers that hindered the applicability of previous approaches. Importantly, this is the first framework for QLSP where a tunable parameter $\eta$ allows the user to control the trade-off between the runtime and the approximation error.
Primary Area: optimization
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Submission Number: 5506
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