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...
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| Vydáno v: | Machine learning: science and technology Ročník 2; číslo 3; s. 35028 |
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| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
01.09.2021
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| ISSN: | 2632-2153, 2632-2153 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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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| ISSN: | 2632-2153 2632-2153 |
| DOI: | 10.1088/2632-2153/ac0616 |