Machine learning methods in quantum computing theory

TL;DR

This paper explores the integration of classical machine learning and quantum computing, introducing new hybrid methods and demonstrating their application on quantum processors for improved quantum state analysis.

Contribution

It presents novel hybrid classical-quantum algorithms, including a multiclass tree tensor network and a neural network approach for quantum tomography, with experimental validation.

Findings

Demonstrated multiclass tree tensor network on IBM quantum processor

Developed a noise-resistant quantum tomography neural network

Showed potential for revealing data dependencies in quantum experiments

Abstract

Classical machine learning theory and theory of quantum computations are among of the most rapidly developing scientific areas in our days. In recent years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. The quantum machine learning includes hybrid methods that involve both classical and quantum algorithms. Quantum approaches can be used to analyze quantum states instead of classical data. On other side, quantum algorithms can exponentially improve classical data science algorithm. Here, we show basic ideas of quantum machine learning. We present several new methods that combine classical machine learning algorithms and quantum computing methods. We demonstrate multiclass tree tensor network algorithm, and its approbation on IBM quantum processor. Also, we introduce neural networks approach to quantum tomography problem. Our tomography method allows us to predict quantum state excluding noise influence. Such classical-quantum approach can be applied in various experiments to reveal latent dependence between input data and output measurement results.