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Variational quantum tensor networks classifiers
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DOI：10.1016/j.neucom.2021.04.074.png)
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Abstract
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Tensor networks (TN) are a method of decomposing high-rank tensors into tractable lower rank. In this paper, we present a classification algorithm for variational quantum tensor networks (VQTN), which has higher performance on near-term processors. Motivated by the hybrid quantum-classical architecture, the truncated quantum tensor networks (QTN) outputs are fed into a classical neural network. We then utilize kernel encoding, circuit models, multiple readouts, and stochastic gradient descent to achieve shallow quantum circuits. Finally, we deploy the QTN and VQTN algorithms on the TensorFlow Quantum processor by using the Iris and MNIST data sets. Our algorithm experimentally costs only half of the qubits with an average accuracy of 93.72%. Compared with the QTN algorithm, the accuracy is improved by 7.71%. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Tensor networks
Quantum computing
Quantum machine learning
Quantum algorithm
Quantum circuit
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