Reordering Decision Diagrams for Quantum Computing Is Harder Than You Might Think

Hillmich, Stefan; Burgholzer, Lukas; Stögmüller, Florian; Wille, Robert

doi:10.1007/978-3-031-09005-9_7

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…However, unlike AADD, it only factors according to the structure of the Bayesian network, which sacrifices succinctness but ensures exact representation of the floating point numbers. The latter can be quite important in practice, as rounding errors from manipulation operations can rapidly propagate in the discrete data structure, resulting in numerical instability [80,125,163].…”

Section: Discussionmentioning

confidence: 99%

Probabilistic inference using partitioned bayesian networks : introducing a compositional framework

Dal

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Previous chapters have introduced many theoretical concepts that help improve the performance of probabilistic inference. These concepts were implemented in a tool called ParaGnosis, an open-source tool that supports inference queries on discrete Bayesian networks through WMC. This tool can be found on GitHub 1 and Zenodo 2 to ensure long-term availability and has a DOI 3 . ParaGnosis was awarded the Functional, Reusable and Available badges by TACAS 2023: the 29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. This chapter is based on [39]. My contributions are the writing of the paper and I am the creator of ParaGnosis, where concepts are implemented from [40-43]. Chapter 8: Model-based Probabilistic Diagnosis in Large ModelsModel-based diagnostics or diagnosis is concerned with diagnosing faults or malfunction of physical or cyber-physical systems using a model of the structure and behavior of a real-world system. As cyber-physical systems can be extremely large and complex the associated models can be equally large and complex, imposing a challenge on the computational feasibility of reasoning with such models. In this chapter, we create such a system and evaluate its performance and diagnostic behavior.This chapter is based on a submitted article. My contributions are the writing of the paper and performing the empirical evaluation using ParaGnosis. Chapter 9: Discussion And ConclusionsResearch presented in this thesis is briefly summarized, and brought into context of what was achieved and other research. We also look toward future work and hypothesize on promising research directions.

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Section: Discussionmentioning

confidence: 99%

Probabilistic inference using partitioned bayesian networks : introducing a compositional framework

Dal

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ParaGnosis: A Tool for Parallel Knowledge Compilation

Dal

Laarman

Lucas

2023

Lecture Notes in Computer Science

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Quantum Computing Significance on Multidimensional Data

Hema

Arowolo

2023

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Quantum computing is an emerging field of study and gains importance due to the fact that with the introduction of quantum computers, many challenges and changes are presented for the existing algorithms. The main reason for this is the exponential speed of such computers. This study analyzes some of the benefits and implications of quantum computing on geometrical problems such as the multidimensional search for window queries with R-Trees. A review of the window query on R-Trees in classical computing is done to consider its adaptability to quantum computers by applying the Grover's quantum search algorithm from a theoretical point of view. Thereby, the query time complexity in worst-case scenarios could be improved to quadratic search time.

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Reordering Decision Diagrams for Quantum Computing Is Harder Than You Might Think

Cited by 3 publications

References 26 publications

Probabilistic inference using partitioned bayesian networks : introducing a compositional framework

Probabilistic inference using partitioned bayesian networks : introducing a compositional framework

ParaGnosis: A Tool for Parallel Knowledge Compilation

Quantum Computing Significance on Multidimensional Data

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