# Improving quantum linear system solvers via a gradient descent perspective

@inproceedings{Gribling2021ImprovingQL, title={Improving quantum linear system solvers via a gradient descent perspective}, author={Sander Gribling and Iordanis Kerenidis and D'aniel Szil'agyi}, year={2021} }

Solving systems of linear equations is one of the most important primitives in quantum computing that has the potential to provide a practical quantum advantage in many different areas, including in optimization, simulation, and machine learning. In this work, we revisit quantum linear system solvers from the perspective of convex optimization, and in particular gradient descent-type algorithms. This leads to a considerable constant-factor improvement in the runtime (or, conversely, a several…

## 2 Citations

### A Survey of Quantum Computing for Finance

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A comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning, describing how these solutions, adapted to work on a quantum computer, can potentially help to solve financial problems more efficiently and accurately.

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- Computer Science
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An algorithm for Hermitian-operator function design from an oracle given by the unitary evolution with respect to that operator at a ﬁxed time is presented, which implements a Fourier approximation of the target function based on the iteration of a basic sequence of single-qubit gates, for which it is proved the expressibility.

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