Quantum Materials Machine Learning Model
%20models%20are%20referred%20to%20as%20black%20box%20models%2C%20providing%20no%20real%20insights%20into%20why%20a%20prediction%20is%20made.%20Feature%20importance%20and%20explainability%20are%20important%20for%20increasing%20transparency%20and%20trust%20in%20ML%20models%2C%20particularly%20in%20settings%20such%20as%20healthcare%20and%20finance.%20With%20quantum%20computing's%20unique%20capabilities%2C%20such%20as%20leveraging%20quantum%20mechanical%20phenomena%20like%20superposition%2C%20which%20can%20be%20combined%20with%20ML%20techniques%20to%20create%20the%20field%20of%20Quantum%20Machine%20Learning%20(QML)%2C%20and%20such%20techniques%20may%20be%20applied%20to%20QML%20models.%20This%20article%20explores%20feature%20importance%20and%20explainability%20insights%20in%20QML%20compared%20to%20Classical%20ML%20models.%20Utilizing%20the%20widely%20recognized%20Iris%20dataset%2C%20classical%20ML%20algorithms%20such%20as%20SVM%20and%20Random%20Forests%2C%20are%20compared%20against%20hybrid%20quantum%20counterparts%2C%20implemented%20via%20IBM's%20Qiskit%20platform:%20the%20Variational%20Quantum%20Classifier%20(VQC)%20and%20Quantum%20Support%20Vector%20Classifier%20(QSVC).%20This%20article%20aims%20to%20provide%20a%20comparison%20of%20the%20insights%20generated%20in%20ML%20by%20employing%20permutation%20and%20leave%20one%20out%20feature%20importance%20methods%2C%20alongside%20ALE%20(Accumulated%20Local%20Effects)%20and%20SHAP%20(SHapley%20Additive%20exPlanations)%20explainers.)
%20and%20learning%20if%20they%20are%20related%20to%20one%20another.%20For%20this%2C%20we%20develop%20a%20geometric%20quantum%20machine%20learning%20(GQML)%20approach%20with%20embedded%20symmetries%20that%20allows%20for%20the%20classification%20of%20similar%20and%20dissimilar%20pairs%20based%20on%20global%20correlations%2C%20and%20enables%20generalization%20from%20just%20a%20few%20samples.%20Unlike%20GQML%20algorithms%20developed%20to%20date%2C%20we%20propose%20to%20focus%20on%20symmetry-aware%20measurement%20adaptation%20that%20outperforms%20unitary%20parametrizations.%20We%20compare%20GQML%20for%20similarity%20testing%20against%20classical%20deep%20neural%20networks%20and%20convolutional%20neural%20networks%20with%20Siamese%20architectures.%20We%20show%20that%20quantum%20networks%20largely%20outperform%20their%20classical%20counterparts.%20We%20explain%20this%20difference%20in%20performance%20by%20analyzing%20correlated%20distributions%20used%20for%20composing%20our%20dataset.%20We%20relate%20the%20similarity%20testing%20with%20problems%20that%20showcase%20a%20proven%20maximal%20separation%20between%20the%20BQP%20complexity%20class%20and%20the%20polynomial%20hierarchy.%20While%20the%20ability%20to%20achieve%20advantage%20largely%20depends%20on%20how%20data%20are%20loaded%2C%20we%20discuss%20how%20similar%20problems%20can%20benefit%20from%20quantum%20machine%20learning.)
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