Quantum autoencoders with enhanced data encoding
We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a fea...
Saved in:
| Published in: | Machine learning: science and technology Vol. 2; no. 3; p. 35028 |
|---|---|
| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
01.09.2021
|
| ISSN: | 2632-2153, 2632-2153 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a feature vector that characterizes such a model. We assess the validity of the method in simulations by compressing ground states of the Ising model and classical handwritten digits. The results show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources, but at the expense of additional classical optimization. Therefore, EF-QAE makes the task of compressing quantum information better suited to be implemented in near-term quantum devices. |
|---|---|
| ISSN: | 2632-2153 2632-2153 |
| DOI: | 10.1088/2632-2153/ac0616 |