Noise-resistant adaptive Hamiltonian learning

TL;DR

This paper introduces a noise-resistant adaptive Hamiltonian learning model that enhances quantum neural network robustness on NISQ devices, enabling accurate data analysis and classification despite noise interference.

Contribution

It proposes a novel adaptive quantum circuit and a noise-resistant quantum neural network that significantly improve noise robustness in quantum machine learning.

Findings

RQNN achieves 98% accuracy under amplitude damping noise

The model effectively simulates mathematical functions on NISQ devices

Enhanced noise robustness expands quantum machine learning applications

Abstract

Mitigating and reducing noise influence is crucial for obtaining precise experimental results from noisy intermediate-scale quantum (NISQ) devices. In this work, an adaptive Hamiltonian learning (AHL) model for data analysis and quantum state simulation is proposed to overcome problems such as low efficiency and the noise influence of quantum machine learning algorithms. First, an adaptive parameterized quantum circuit with noise resistant ability is constructed by decomposing the unitary operators that include penalty Hamiltonian in the topological quantum system. Then, a noise-resistant quantum neural network (RQNN) based on AHL is developed, which improves the noise robustness of the quantum neural network by updating iterative parameters. Finally, the experiments on Paddle Quantum demonstrate that RQNN can simulate the mathematical function and get accurate classification results on NISQ devices. Compared with the quantum neural network, RQNN ensures high accuracy with the same non-linear discrete data classification under the impact of amplitude damping noise, with an accuracy of 98.00 $\%$. It provides new possibilities for solving practical issues on NISQ devices and also benefits in the resolution of increasingly complicated problems, which will expand the range of potential applications for quantum machine learning models in the future.