Connecting Quantum Computing with Classical Stochastic Simulation

Fuente: arXiv
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Hauptverfasser: Blanchet, Jose, Squillante, Mark S., Szegedy, Mario, Wang, Guanyang
Format: Preprint
Veröffentlicht: 2025
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author Blanchet, Jose
Squillante, Mark S.
Szegedy, Mario
Wang, Guanyang
author_facet Blanchet, Jose
Squillante, Mark S.
Szegedy, Mario
Wang, Guanyang
contents This tutorial paper introduces quantum approaches to Monte Carlo computation with applications in computational finance. We outline the basics of quantum computing using Grover's algorithm for unstructured search to build intuition. We then move slowly to amplitude estimation problems and applications to counting and Monte Carlo integration, again using Grover-type iterations. A hands-on Python/Qiskit implementation illustrates these concepts applied to finance. The paper concludes with a discussion on current challenges in scaling quantum simulation techniques.
format Preprint
id arxiv_https___arxiv_org_abs_2509_18614
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Connecting Quantum Computing with Classical Stochastic Simulation
Blanchet, Jose
Squillante, Mark S.
Szegedy, Mario
Wang, Guanyang
Quantum Physics
Numerical Analysis
Computational Finance
Computation
This tutorial paper introduces quantum approaches to Monte Carlo computation with applications in computational finance. We outline the basics of quantum computing using Grover's algorithm for unstructured search to build intuition. We then move slowly to amplitude estimation problems and applications to counting and Monte Carlo integration, again using Grover-type iterations. A hands-on Python/Qiskit implementation illustrates these concepts applied to finance. The paper concludes with a discussion on current challenges in scaling quantum simulation techniques.
title Connecting Quantum Computing with Classical Stochastic Simulation
topic Quantum Physics
Numerical Analysis
Computational Finance
Computation
url https://arxiv.org/abs/2509.18614