Hierarchical quantum classifiers

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.Machine learning: Quantum networks for classical and quantum dataQuantum algorithms with hierarchical tensor network structures may provide an efficient approach to machine learning using quantum computers. Recent theoretical work has indicated that quantum algorithms could have an advantage over classical methods for the linear algebra computations involved in machine learning. At the same time, mathematical structures called tensor networks, with some similarities to neural networks, have been shown to represent quantum states and circuits that can be efficiently evaluated. Edward Grant from University College London and colleagues from the UK and China have shown how quantum algorithms based on two tensor network structures can be used to classify both classical and quantum data. If implemented on a large scale quantum computer, their approach may enable classification of two-dimensional images and entangled quantum data more efficiently than is possible with classical methods.