Quantum-Classical Machine learning by Hybrid Tensor Networks
arXiv:2005.09428
Abstract
Tensor networks (TN) have found a wide use in machine learning, and in particular, TN and deep learning bear striking similarities. In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning. We first analyze the limitations of regular tensor networks in the applications of machine learning involving the representation power and architecture scalability. We conclude that in fact the regular tensor networks are not competent to be the basic building blocks of deep learning. Then, we discuss the performance of HTN which overcome all the deficiency of regular tensor networks for machine learning. In this sense, we are able to train HTN in the deep learning way which is the standard combination of algorithms such as Back Propagation and Stochastic Gradient Descent. We finally provide two applicable cases to show the potential applications of HTN, including quantum states classification and quantum-classical autoencoder. These cases also demonstrate the great potentiality to design various HTN in deep learning way.
References in corpus (11)
- Matrix Product States, Projected Entangled Pair States, and variational renormalization group methods for quantum spin systems
- A generative modeling approach for benchmarking and training shallow quantum circuits
- Unsupervised Generative Modeling Using Matrix Product States
- Equivalence of restricted Boltzmann machines and tensor network states
- Unsupervised Machine Learning on a Hybrid Quantum Computer
- Training of Quantum Circuits on a Hybrid Quantum Computer
- Advances on Tensor Network Theory: Symmetries, Fermions, Entanglement, and Holography
- TensorNetwork: A Library for Physics and Machine Learning
- TensorNetwork for Machine Learning
- Tangent-Space Gradient Optimization of Tensor Network for Machine Learning
- Shortcut Matrix Product States and its applications