Quantum Phase MRI Simulation: Finite Mathematics & Circuit Derivations

Tetsuro Nishino

Cartik Sharma

Shrivatsa Vaman Gudi, Parimal Nitin Ganorkar, Sasikala R

The classification of Autism Spectrum Disorder (ASD) using neuroimaging techniques is still not an easy task, as the features have a high dimension, inter-site variability, and a small number of labeled samples. In this paper, HybridVQC, a quantum-inspired hybrid neural architecture, incorporates mathematical concepts of quantum circuits into a fully classical, learning architecture that can be executed on a GPU. ABIDE Structural MRI slices are fed through a pretrained backbone of ResNet-18 to obtain deep representations, and the deep representations are further shrunk to 16 principal components in Principal Component Analysis (PCA). The low features are subsequently applied to a special QuantumLikeLayer, which uses trigonometric encoding and dense mixing of features in order to simulate quantum rotation and entanglement effects on ordinary CUDA. The results of experiments with 1,693 structural MRI slices reveal that the maximum validation and test accuracy is 80.63 and 75.0, respectively, versus 56% of a classical SVM baseline using the same parameters. The findings point to quantum-inspired non-linear transformations have the potential to enhance feature separability and training stability in neuroimaging classification, and do not use quantum simulators or physical quantum systems.

Masahiro Fujita, Yusuke Kimura

George F. Viamontes, Igor L. Markov, John P. Hayes

Shrivatsa Vaman Gudi, Parimal Nitin Ganorkar, Sasikala R

The classification of Autism Spectrum Disorder (ASD) using neuroimaging techniques is still not an easy task, as the features have a high dimension, inter-site variability, and a small number of labeled samples. In this paper, HybridVQC, a quantum-inspired hybrid neural architecture, incorporates mathematical concepts of quantum circuits into a fully classical, learning architecture that can be executed on a GPU. ABIDE Structural MRI slices are fed through a pretrained backbone of ResNet-18 to obtain deep representations, and the deep representations are further shrunk to 16 principal components in Principal Component Analysis (PCA). The low features are subsequently applied to a special QuantumLikeLayer, which uses trigonometric encoding and dense mixing of features in order to simulate quantum rotation and entanglement effects on ordinary CUDA. The results of experiments with 1,693 structural MRI slices reveal that the maximum validation and test accuracy is 80.63 and 75.0, respectively, versus 56% of a classical SVM baseline using the same parameters. The findings point to quantum-inspired non-linear transformations have the potential to enhance feature separability and training stability in neuroimaging classification, and do not use quantum simulators or physical quantum systems.