Quantum artificial neural network architectures and components
Information Sciences—Informatics and Computer Science: An International Journal - Special Issue on Quantum Computing and Neural Information Processing
OpenMP: An Industry-Standard API for Shared-Memory Programming
IEEE Computational Science & Engineering
Minimizing development and maintenance costs in supporting persistently optimized BLAS
Software—Practice & Experience - Research Articles
A flocking based algorithm for document clustering analysis
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
Kernel-based classification using quantum mechanics
Pattern Recognition
General purpose molecular dynamics simulations fully implemented on graphics processing units
Journal of Computational Physics
Graphical Processing Units for Quantum Chemistry
Computing in Science and Engineering
Browsing a Large Collection of Community Photos Based on Similarity on GPU
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model
Journal of Computational Physics
A Chunking Method for Euclidean Distance Matrix Calculation on Large Dataset Using Multi-GPU
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Distilling relevant documents by means of dynamic quantum clustering
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Empowering Visual Categorization With the GPU
IEEE Transactions on Multimedia
Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks
IEEE Transactions on Neural Networks
Self organization of a massive document collection
IEEE Transactions on Neural Networks
| Hi-index | 31.45 |
Clustering methods in machine learning may benefit from borrowing metaphors from physics. Dynamic quantum clustering associates a Gaussian wave packet with the multidimensional data points and regards them as eigenfunctions of the Schrodinger equation. The clustering structure emerges by letting the system evolve and the visual nature of the algorithm has been shown to be useful in a range of applications. Furthermore, the method only uses matrix operations, which readily lend themselves to parallelization. In this paper, we develop an implementation on graphics hardware and investigate how this approach can accelerate the computations. We achieve a speedup of up to two magnitudes over a multicore CPU implementation, which proves that quantum-like methods and acceleration by graphics processing units have a great relevance to machine learning.