US 11,741,391 B2
Quantum topological classification
Tal Kachman, Haifa (IL); Kenneth Lee Clarkson, Madison, NJ (US); Mark S. Squillante, Greenwich, CT (US); Lior Horesh, North Salem, NY (US); and Ismail Yunus Akhalwaya, Emmarentia (ZA)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Sep. 19, 2019, as Appl. No. 16/576,046.
Prior Publication US 2021/0256414 A1, Aug. 19, 2021
Int. Cl. G06N 20/00 (2019.01); G06N 10/00 (2022.01); G06N 10/60 (2022.01); G06F 18/20 (2023.01); G06F 18/2415 (2023.01); G06V 10/764 (2022.01)
CPC G06N 20/00 (2019.01) [G06F 18/24155 (2023.01); G06F 18/29 (2023.01); G06N 10/00 (2019.01); G06N 10/60 (2022.01); G06V 10/764 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a topological component that employs one or more quantum computing operations to identify one or more persistent homology features of a topological simplicial structure; and
an algorithmic engine component that employs at least one of a variational quantum eigensolver algorithm or a hybrid Bayesian phase learning algorithm to determine at least one of a kernel of a graph Laplacian matrix or one or more Betti numbers corresponding to one or more quantum representations of the topological simplicial structure at a defined homological persistent scale; and
a topological classifier component that employs one or more machine learning models to classify the topological simplicial structure based on the one or more persistent homology features.