US 11,657,312 B2
Short-depth active learning quantum amplitude estimation without eigenstate collapse
Ismail Yunus Akhalwaya, Emmarentia (ZA); Kenneth Clarkson, Madison, NJ (US); Lior Horesh, North Salem, NY (US); Mark S. Squillante, Greenwich, CT (US); Shashanka Ubaru, Ossining, NY (US); and Vasileios Kalantzis, White Plains, NY (US)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jan. 31, 2020, as Appl. No. 16/778,878.
Prior Publication US 2023/0114370 A1, Apr. 13, 2023
Int. Cl. G06N 10/00 (2022.01); G06N 20/00 (2019.01); G06F 17/11 (2006.01)
CPC G06N 10/00 (2019.01) [G06F 17/11 (2013.01); G06N 20/00 (2019.01)] 25 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 learning component that utilizes stochastic inference to determine an expectation value during a first time interval based on an uncollapsed eigenvalue pair retrieved by an active learning process, wherein the first time interval is less than a second time interval where the uncollapsed eigenvalue pair is retrieved without use of the active learning process; and
an encoding component that encodes the expectation value associated with a quantum state.