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)
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.