Topological Data Analysis on Noisy Quantum Computers
Ismail Akhalwaya, Shashanka Ubaru, et al.
ICLR 2024
We introduce a new convolution kernel for labeled ordered trees with arbitrary subgraph features, and an efficient algorithm for computing the kernel with the same time complexity as that of the parse tree kernel. The proposed kernel is extended to allow mutations of labels and structures without increasing the order of computation time. Moreover, as a limit of generalization of the tree kernels, we show a hardness result in computing kernels for unordered rooted labeled trees with arbitrary subgraph features.
Ismail Akhalwaya, Shashanka Ubaru, et al.
ICLR 2024
Ankit Vishnubhotla, Charlotte Loh, et al.
NeurIPS 2023
David W. Jacobs, Daphna Weinshall, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dzung Phan, Vinicius Lima
INFORMS 2023