Deep learning

Physics

Mathematics

Algorithm

Quantum circuit

Quantum network

Artificial intelligence

MNIST database

Quantum machine learning

Computer science

Quantum computer

Quantum mechanics

Theoretical computer science

Binary number

Quantum

Arithmetic

Classifier (UML)

npj Quantum Information

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.

Hierarchical quantum classifiers