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Variational quantum extreme learning machine
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AI
DOI：10.1016/j.neucom.2022.09.068.png)
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Abstract
En 中文
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 exponen-tially 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 transfor-mation 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 anal-ysis 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.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Extreme learning machine
Quantum neural network
Variational quantum algorithm