An end-to-end trainable hybrid classical-quantum classifier

Abstract

We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks. This architecture allows for the classical and quantum parts of the model to be trained simultaneously, providing an end-to-end training framework. We show that compared to the principal component analysis, a tensor network based on the matrix product state with low bond dimensions performs better as a feature extractor for the input data of the variational quantum circuit in the binary and ternary classification of MNIST and Fashion-MNIST datasets. The architecture is highly adaptable and the classical-quantum boundary can be adjusted according the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits.

arXiv comment13 pages, 5 figures. arXiv admin note: text overlap with arXiv:2011.14651

References in corpus (33)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Quantum Computing in the NISQ era and beyond
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Quantum Machine Learning
• A variational eigenvalue solver on a quantum processor
• Quantum Mechanics helps in searching for a needle in a haystack
• The density-matrix renormalization group in the age of matrix product states
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Supervised learning with quantum enhanced feature spaces
• The theory of variational hybrid quantum-classical algorithms
• A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Parameterized quantum circuits as machine learning models
• Thermodynamic limit of the density matrix renormalization for the spin-1 Heisenberg chain
• Evaluating analytic gradients on quantum hardware
• Circuit-centric quantum classifiers
• Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
• Quantum Computational Supremacy
• Tensor networks for complex quantum systems
• Quantum generative adversarial networks
• Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
• The Expressive Power of Parameterized Quantum Circuits
• Towards Quantum Machine Learning with Tensor Networks
• Unsupervised Generative Modeling Using Matrix Product States
• Equivalence of restricted Boltzmann machines and tensor network states
• Quantum Entanglement in Deep Learning Architectures
• Entanglement in a quantum annealing processor
• Tree Tensor Networks for Generative Modeling
• Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
• Learning Relevant Features of Data with Multi-scale Tensor Networks
• Generative Tensor Network Classification Model for Supervised Machine Learning
• Probabilistic Modeling with Matrix Product States

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• Image Classification by Throwing Quantum Kitchen Sinks at Tensor Networks