Adiabatic Quantum Computing for Binary Clustering

2017/06/17 by Christian Bauckhage, Eduardo Barreto Brito, Bauckhage, Christian +9
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Quantum many-body systems

paper · pdf · doi:10.48550/arxiv.1706.05528

openalex publication_date 2017/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Quantum algorithms for supervised and unsupervised machine learning
• Least squares quantization in PCM
• Quantum machine learning

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