论文标题
带有增强数据编码的量子自动编码器
Quantum autoencoders with enhanced data encoding
论文作者
论文摘要
我们提出了增强的功能量子自动编码器或EF-QAE，这是一种能够压缩具有较高保真度的不同模型的量子状态的变异量子算法。该算法的关键思想是定义一个参数化的量子电路，该电路取决于可调参数和表征这种模型的特征向量。我们通过压缩ISING模型和经典手写数字的基接地状态来评估模拟中方法的有效性。结果表明，与标准量子自动编码器相比，EF-QAE使用相同数量的量子资源来提高性能，但以其他经典优化为代价。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施。
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.
