Quantum Computing & Clustering Structure !!
Mimicking Human Cognition into a Quantum State.
Unsupervised Learning finds a natural fit with quantum computing.
Quantum states are also becoming capable of self analysis.
As they can reveal their own eigenstructure.
Quantum classification relies on ideas similar to those found in quantum unsupervised learning.
This leads to efficient quantum principal component analysis and quantum manifold learning.
Using quantum random access memory it is possible to move beyond low dimensional embedding.
The quantum random access memory and the quick calculation of dot products and Euclidean distances are crucial to efficient supervised algorithms.
This again enables the use of efficient clustering methods, such as quantum $K$-means and $K$-medians and quantum hierarchical clustering.
A quantum computer operates by controlling the behavior of fundamental subatomic particles like photons and electrons.
But unlike atoms, molecules or people,subatomic particles are notoriously unruly.
