Quantum autoencoders with enhanced data encoding

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.

6 + 3 pages, 10 figures

References in corpus (2)

• Variational Quantum Eigensolver for Frustrated Quantum Systems
• Variational Simulation of Schwinger's Hamiltonian with Polarisation Qubits

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