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20202025
most citedFeature Selection on Quantum Computers

58 citations · 107 across the 12 of their papers we have counts for

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9 papers · 1 filter

quant-ph2024

Predicting Machining Stability with a Quantum Regression Model

Sascha Mücke, Felix Finkeldey, Nico Piatkowski +2

In this article, we propose a novel quantum regression model by extending the Real-Part Quantum SVM. We apply our model to the problem of stability limit prediction in milling proc…

quant-ph2024

Investigating the Relation Between Problem Hardness and QUBO Properties

Thore Gerlach, Sascha Mücke

Combinatorial optimization problems, integral to various scientific and industrial applications, often vary significantly in their complexity and computational difficulty. Transfor…

quant-ph2024

A Simple QUBO Formulation of Sudoku

Sascha Mücke

This article describes how to solve Sudoku puzzles using Quadratic Unconstrained Binary Optimization (QUBO). To this end, a QUBO instance with 729 variables is constructed, encodin…

quant-ph2023

Efficient Light Source Placement using Quantum Computing

Sascha Mücke, Thore Gerlach

NP-hard problems regularly come up in video games, with interesting connections to real-world problems. In the game Minecraft, players place torches on the ground to light up dark…

quant-ph2023

Optimum-Preserving QUBO Parameter Compression

Sascha Mücke, Thore Gerlach, Nico Piatkowski

Quadratic unconstrained binary optimization (QUBO) problems are well-studied, not least because they can be approached using contemporary quantum annealing or classical hardware ac…

quant-ph2023★ 20 cited

Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning

Raoul Heese, Thore Gerlach, Sascha Mücke +3

Methods of artificial intelligence (AI) and especially machine learning (ML) have been growing ever more complex, and at the same time have more and more impact on people's lives.…

quant-ph2022★ 58 cited

Feature Selection on Quantum Computers

Sascha Mücke, Raoul Heese, Sabine Müller +2

In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step.…

quant-ph2021★ 14 cited

On the effects of biased quantum random numbers on the initialization of artificial neural networks

Raoul Heese, Moritz Wolter, Sascha Mücke +2

Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy interme…

quant-ph2020★ 15 cited

Quantum Circuit Evolution on NISQ Devices

Lukas Franken, Bogdan Georgiev, Sascha Mücke +4

Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empir…