This pull request aims at adding the multi-fidelity learning approach called CQML, which is documented in the paper "Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited":
the core part of the new code are the new files qml/models/cqml2d.py
and qml/models/cqml.py, which contain a 2d CQML implementation
and a 2d/3d/nd CQML implementation. The 2d implementation follows the
simplified idea presented in Section 3.4 in the paper, while the 2d/3d/nd
CQML implementation is documented in Section 3.5
examples/cqml2d_CI9.py is the code, which was used to create
2d CQML results on the CI9 data set in the paper
examples/cqml_QM7b.py, examples/cqml_in2d*.py are the codes
that were used to create the 2d/3d results for the QM7b data set
in the paper
the CI9 and the QM7b data set are included in the examples directory
This pull request aims at adding the multi-fidelity learning approach called CQML, which is documented in the paper "Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited":
the core part of the new code are the new files qml/models/cqml2d.py and qml/models/cqml.py, which contain a 2d CQML implementation and a 2d/3d/nd CQML implementation. The 2d implementation follows the simplified idea presented in Section 3.4 in the paper, while the 2d/3d/nd CQML implementation is documented in Section 3.5
examples/cqml2d_CI9.py is the code, which was used to create 2d CQML results on the CI9 data set in the paper
examples/cqml_QM7b.py, examples/cqml_in2d*.py are the codes that were used to create the 2d/3d results for the QM7b data set in the paper
the CI9 and the QM7b data set are included in the examples directory