Hybrid quantum learning with data reuploading on a small-scale superconducting quantum simulator
Abstract
Supervised quantum learning is an emergent multidisciplinary domain bridging between variational quantum algorithms and classical machine learning. Here, we study experimentally a hybrid classifier model using quantum hardware simulator (a linear array of four superconducting transmon artificial atoms) trained to solve multilabel classification and image recognition problems. We train a quantum circuit on simple binary and multilabel tasks, achieving classification accuracy around 95%, and a hybrid quantum model with data reuploading with accuracy around 90% when recognizing handwritten decimal digits. Finally, we analyze the inference time in experimental conditions and compare the performance of the studied quantum model with known classical solutions.
- Publication:
-
Physical Review A
- Pub Date:
- January 2024
- DOI:
- 10.1103/PhysRevA.109.012411
- arXiv:
- arXiv:2305.02956
- Bibcode:
- 2024PhRvA.109a2411T
- Keywords:
-
- Quantum Physics;
- Condensed Matter - Disordered Systems and Neural Networks
- E-Print:
- 11 pages, 6 figures