sf.apps.data.feature.QM9MC

class QM9MC[source]

Bases: strawberryfields.apps.data.feature.FeatureDataset

Monte-Carlo estimated feature vectors of 1100 randomly-chosen molecules from the QM9 dataset.

The QM9 dataset is widely used in benchmarking performance of machine learning models in estimating molecular properties [6], [7].

Coulomb matrices were used as adjacency matrices to represent molecules in this case.

The exactly-calculated feature vectors of certain orbits of these 1100 molecules are also available in the QM9Exact class.

method = "mc"
n_mean = 6
unit = "events"
unit_data = [[2, 2], [4, 2], [6, 2]]

method

n_mean

unit

unit_data

method = 'mc'
n_mean = 6
unit = 'events'
unit_data = [[2, 2], [4, 2], [6, 2]]