QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks

The state-of-the-art machine learning (ML), particularly based on deep neural networks (DNN), has enabled a wide spectrum of successful applications ranging from the everyday deployment of speech recognition [1] and computer vision [2] through to the frontier of scientific research in synthetic biology [8]. Despite rapid theoretical and empirical progress in DNN based regression and classification [9], DNN training algorithms are computationally expensive for many new scientific applications, such as new drug discovery [10], which requires computational resources that are beyond the computational limits of classical hardwares [11]. Fortunately, the imminent advent of quantum computing devices opens up new possibilities of exploiting quantum machine learning (QML) [12, 13, 14, 15, 16, 17] to improve the computational efficiency of ML algorithms in the new scientific domains. Although the exploitation of quantum computing devices to carry out QML is still in its initial exploratory stages, the rapid development in quantum hardware has motivated advances in quantum neural networks (QNN) to run in noisy intermediate-scale quantum (NISQ) devices [18, 19, 20, 21]. A NISQ device means that not enough qubits could be spared for quantum error correction, and the imperfect qubits have to be directly used at the physical layer.