Experimental realization of a quantum image classifier via tensor-network-based machine learning

Abstract

Quantum machine learning aspires to overcome intractability that currently limits its applicability to practical problems. However, quantum machine learning itself is limited by low effective dimensions achievable in state-of-the-art experiments. Here we demonstrate highly successful classifications of real-life images using photonic qubits, combining a quantum tensor-network representation of hand-written digits and entanglement-based optimization. Specifically, we focus on binary classification for hand-written zeroes and ones, whose features are cast into the tensor-network representation, further reduced by optimization based on entanglement entropy and encoded into two-qubit photonic states. We then demonstrate image classification with a high success rate exceeding 98%, through successive gate operations and projective measurements. Although we work with photons, our approach is amenable to other physical realizations such as nitrogen-vacancy centers, nuclear spins and trapped ions, and our scheme can be scaled to efficient multi-qubit encodings of features in the tensor-product representation, thereby setting the stage for quantum-enhanced multi-class classification.

arXiv comment10 pages, 7 figures

References in corpus (35)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum Machine Learning
• Supervised learning with quantum enhanced feature spaces
• Quantum support vector machine for big data classification
• Matrix Product States, Projected Entangled Pair States, and variational renormalization group methods for quantum spin systems
• Quantum machine learning in feature Hilbert spaces
• Quantum Convolutional Neural Networks
• Quantum Entanglement in Neural Network States
• Renormalization and tensor product states in spin chains and lattices
• Sequential generation of entangled multi-qubit states
• Towards Quantum Machine Learning with Tensor Networks
• Experimental Realization of Quantum Artificial Intelligence
• Unsupervised Generative Modeling Using Matrix Product States
• Entanglement-Based Machine Learning on a Quantum Computer
• Training of Quantum Circuits on a Hybrid Quantum Computer
• Quantum Entanglement in Deep Learning Architectures
• Machine Learning for Precise Quantum Measurement
• Lecture Notes of Tensor Network Contractions
• Quantum generative adversarial learning in a superconducting quantum circuit
• Experimental Machine Learning of Quantum States
• Tree Tensor Networks for Generative Modeling
• Diagonalizing transfer matrices and matrix product operators: a medley of exact and computational methods
• Quantum machine learning over infinite dimensions
• Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
• Learning Relevant Features of Data with Multi-scale Tensor Networks
• Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning
• Universal quantum computation in deocoherence-free subspace with neutral atoms
• Quantum walk on a line for a trapped ion
• Machine Learning Spatial Geometry from Entanglement Features
• Entanglement-enhanced quantum metrology in a noisy environment
• Machine Learning Topological Phases with a Solid-state Quantum Simulator
• Centrality measure based on continuous-time quantum walks and experimental realization
• Matrix Product Operators for Sequence to Sequence Learning
• Generative Tensor Network Classification Model for Supervised Machine Learning
• Protocol for implementing quantum nonparametric learning with trapped ions

Cited by in corpus (6)

• Efficient measure for the expressivity of variational quantum algorithms
• Tensor networks for quantum machine learning
• Tensor networks for interpretable and efficient quantum-inspired machine learning
• Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations
• Near-Term Advances in Quantum Natural Language Processing
• Modeling and benchmarking quantum optical neurons for efficient neural computation