A quantum algorithm for minimal spanning tree

Aghaei, Mohammad Reza Soltan; Zukarnain, Zuriati Ahmad; Mamat, Ali; Zainuddin, Hishamuddin

doi:10.1109/itsim.2008.4632038

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The Case For Quantum Computing In Finance1

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2024

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Cited by 6 publications

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“…By contrast, the best classical algorithm requires O(N/2) steps. This algorithm can be adapted to solve optimization problems, such as finding a Minimum Spanning Tree [19], maximizing flow-like variables [20], and implementing Monte Carlo methods [9]. Alternatively, the Quantum Approximate Optimization Algorithm (QAOA) finds a "good solution" (i.e: one with a minimum quality) to an optimization problem in a polynomial time [21].…”

Section: The Case For Quantum Computing In Financementioning

confidence: 99%

Quantum computing for finance: Overview and prospects

Orús

Mugel²,

Lizaso³

2019

Reviews in Physics

436

232

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We discuss how quantum computation can be applied to financial problems, providing an overview of current approaches and potential prospects. We review quantum optimization algorithms, and expose how quantum annealers can be used to optimize portfolios, find arbitrage opportunities, and perform credit scoring. We also discuss deep-learning in finance, and suggestions to improve these methods through quantum machine learning. Finally, we consider quantum amplitude estimation, and how it can result in a quantum speed-up for Monte Carlo sampling. This has direct applications to many current financial methods, including pricing of derivatives and risk analysis. Perspectives are also discussed.

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Section: The Case For Quantum Computing In Financementioning

confidence: 99%