# Adiabatic Quantum Computing for Binary Clustering

@article{Bauckhage2017AdiabaticQC, title={Adiabatic Quantum Computing for Binary Clustering}, author={Christian Bauckhage and Eduardo Brito and Kostadin Cvejoski and C. Ojeda and Rafet Sifa and Stefan Wrobel}, journal={arXiv: Machine Learning}, year={2017} }

Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest. In this paper, we therefore consider this paradigm and discuss how to adopt it to the problem of binary clustering. Numerical simulations demonstrate the feasibility of our approach and illustrate how systems of qubits adiabatically evolve towards a solution.

## 5 Citations

Adiabatic Quantum Computing for Kernel k=2 Means Clustering

- Computer Science, PhysicsLWDA
- 2018

This paper discusses how to accomplish the quest for Ising model (re-)formulations of their underlying objective functions for the problem of kernel binary clustering, and discusses how these models can be solved on an adiabatic quantum computing device.

Adiabatic Quantum Computing for Solving the Weapon Target Assignment Problem

- Computer Science, Physics2021 IEEE 24th International Conference on Information Fusion (FUSION)
- 2021

Overall, the described method is not limited to the context of weapon management but is, with slight modifications to the model Hamiltonian, applicable to relevant problems in data fusion, for example sensor allocation and multi-target data association in tracking applications.

Coreset Clustering on Small Quantum Computers

- Computer ScienceElectronics
- 2021

This work investigates using a new paradigm in hybrid quantum-classical computing to perform $k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset.

Clustering by quantum annealing on the three-level quantum elements qutrits

- Computer Science, PhysicsQuantum Inf. Process.
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The simulation has shown that the clustering problem can be effectively solved on qutrits represented by the spins S = 1, and the advantages of clustering on qUTrits over that on qubits have been demonstrated.

Q-means using variational quantum feature embedding

- Physics, Computer Science
- 2021

This paper proposes a hybrid quantum-classical algorithm that learns a suitable quantum feature map that separates unlabelled data that is originally non linearly separable in the classical space…

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