Hybrid quantum-classical classifier based on tensor network and variational quantum circuit

Abstract

One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding. Traditional principle component analysis (PCA) and neural networks have been used to perform this task; however, the classical and quantum layers are usually trained separately. A framework that allows for a better integration of the two key components is thus highly desirable. Here we introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks, which allows for an end-to-end training. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset. The architecture is highly adaptable and can easily incorporate extra quantum resource when available.

8 pages, 7 figures, and 1 table. Proceeding for the First Workshop on Quantum Tensor Networks in Machine Learning, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada

References in corpus (13)

• Adam: A Method for Stochastic Optimization
• Scikit-learn: Machine Learning in Python
• Supervised learning with quantum enhanced feature spaces
• A Quantum Approximate Optimization Algorithm
• Quantum Circuit Learning
• Thermodynamic limit of the density matrix renormalization for the spin-1 Heisenberg chain
• Circuit-centric quantum classifiers
• Quantum generative adversarial networks
• Transfer learning in hybrid classical-quantum neural networks
• Unsupervised Generative Modeling Using Matrix Product States
• Supervised Learning with Quantum-Inspired Tensor Networks
• TensorNetwork for Machine Learning
• Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design

Cited by in corpus (10)

• Recent advances for quantum classifiers
• Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
• Quantum Architecture Search via Deep Reinforcement Learning
• Hybrid Quantum-Classical Graph Convolutional Network
• Tensor networks for interpretable and efficient quantum-inspired machine learning
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity
• Simulation of a Variational Quantum Perceptron using Grover's Algorithm
• Machine learning with tree tensor networks, CP rank constraints, and tensor dropout
• QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
• BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading