Variational quantum extreme learning machine

Abstract

Extreme learning machine (ELM), with fast training speed and high generalization performance, has been widely used in many fields. However, it becomes inefficient or even impossible to process data with extremely large feature spaces, which is expected to be solved by quantum computing with an exponentially large quantum state space. Here, we propose a novel variational quantum extreme learning machine (VQELM). In detail, we design a special feature mapping method to achieve nonlinear transformation of the input data, replacing the hard-to-construct activation function on quantum devices. Considering that the Harrow-Hassidim-Lloyd algorithm is difficult to solve the ELM parameters on near-term quantum devices, we adopt a variational framework to facilitate implementation on the near-term noisy intermediate scale quantum computer. On both classification and regression tasks, our proposed method outperforms classical ELM in classical simulations. Moreover, the classification tasks achieved on IBM quantum simulator also show comparable classification accuracy. The final analysis shows that our proposed algorithm has an exponential improvement over classical ones for high-dimensional data processing, and is a powerful application of quantum machine learning on near-term quantum devices.