.png){kind=icon}
Return
Quantum autoencoder for image compression through reducing qubits usage
.png){kind=icon}
PRE
AI
DOI：10.1142/S0219749925500339.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Quantum autoencoder, as a quantum machine learning algorithm, provides an important research direction for image compression. This paper proposes a quantum autoencoder specifically designed for image compression. We first use feature mapping to store images as quantum states and propose a new parameterized quantum circuit and a pre-training method. After pre-training, a reference state is determined and the circuit parameters are updated through further training. Finally, we experimentally validate the quantum autoencoder. For reconstructed 10-qubit images, the fidelity reaches 0.98 and the SSIM reaches 0.927. For reconstructed 10-qubit noisy images, the PSNR improves by 1.4dB. Simulation results demonstrate that the proposed quantum autoencoder efficiently compresses quantum images while also possessing denoising capabilities.
Keywords:
Quantum autoencoder
denoising
image compression
parameterized quantum circuit
Journal
IF:
0.8
Papers:
55
Citations:
1.3K
Organization
Cited Papers
ShareSaveResource-efficient high-dimensional subspace teleportation with a quantum autoencoder.png)
SCIENCE ADVANCES
OAAI
SCIENCE ADVANCESIF12.5
OAAIShareSaveShareSaveShareSave
ShareSave
View more