Solving Subset Sum Problems using Quantum Inspired Optimization Algorithms with Applications in Auditing and Financial Data Analysis
Abstract
Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in many real applications. We tackle the problem as a QUBO (quadratic unconstrained binary optimization) problem and show how gradient descent on Hopfield Networks reliably finds solutions for both artificial and real data. We outline how this algorithm can be applied by adiabatic quantum computers (quantum annealers) and specialized hardware (field programmable gate arrays) for digital annealing and run experiments on quantum annealing hardware.
Cite
@article{arxiv.2211.02653,
title = {Solving Subset Sum Problems using Quantum Inspired Optimization Algorithms with Applications in Auditing and Financial Data Analysis},
author = {David Biesner and Thore Gerlach and Christian Bauckhage and Bernd Kliem and Rafet Sifa},
journal= {arXiv preprint arXiv:2211.02653},
year = {2022}
}
Comments
To be published in proceedings of IEEE International Conference on Machine Learning Applications IEEE ICMLA 2022