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.

我们提出了增强特征量子自动编码器或 EF-QAE，这是一种变分量子算法，能够以更高的保真度压缩不同模型的量子态。该算法的关键思想是定义一个参数化的量子电路，该电路依赖于可调参数和表征此类模型的特征向量。我们通过压缩 Ising 模型和经典手写数字的基态来评估该方法在模拟中的有效性。结果表明，与使用相同数量量子资源的标准量子自动编码器相比，EF-QAE 提高了性能，但以额外的经典优化为代价。因此，EF-QAE 使压缩量子信息的任务更适合在近期量子设备中实现。