Quantum autoencoders with enhanced data encoding
- URL: http://arxiv.org/abs/2010.06599v3
- Date: Mon, 12 Jul 2021 16:36:05 GMT
- Title: Quantum autoencoders with enhanced data encoding
- Authors: Carlos Bravo-Prieto
- Abstract summary: Enhanced feature quantum autoencoder, or EF-QAE, is a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity.
We show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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