Hierarchical quantum classifiers

2018/04/30 by Edward Grant, Marcello Benedetti, Shuxiang Cao +5 · 3 citations
Computer Science · Physics and Astronomy · #Binary number #Classifier (UML) #Electronic circuit #Noise (video) #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum algorithm #Quantum circuit #Quantum computer #Quantum many-body systems #quant-ph

paper · pdf · doi:10.1038/s41534-018-0116-9

published as npj Quantum Information 4, 65 (2018)

openalex created_date 2018/04/24 · openalex publication_date 2018/12/10 · arxiv created 2018/12/27 · arxiv updated 2019/01/21 · openalex updated_date 2026/08/05

Abstract

Abstract Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to classify highly entangled quantum states, for which there is no known efficient classical method. We compare performance for several different parameterizations on two classical machine learning datasets, Iris and MNIST, and on a synthetic dataset of quantum states. Finally, we demonstrate that performance is robust to noise and deploy an Iris dataset classifier on the ibmqx4 quantum computer.

Citations

• The quest for a Quantum Neural Network
• A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
• Class of Quantum Many-Body States That Can Be Efficiently Simulated
• Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems
• Quantum support vector machine for big data classification
• Why does deep and cheap learning work so well?
• Unsupervised Machine Learning on a Hybrid Quantum Computer
• Quantum Computing in the NISQ era and beyond
• Towards quantum machine learning with tensor networks
• Deep learning

Cited by

• Tree-tensor-network classifiers for machine learning: From quantum inspired to quantum assisted
• Quantum Machine Learning Tensor Network States
• Quantum noise protects quantum classifiers against adversaries