Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching
Abstract Modern technology and scientific experiments increasingly generate larger and larger amounts of data. This data is sometimes redundant, incomplete or inaccurate and needs to be cleaned and merged with other data before becoming useful for scientific exploration. Hence, entity matching, i.e....
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-88177-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|