Quantum data generation in a denoising model with multiscale entanglement renormalization network

TL;DR

This paper introduces a noise-resistant quantum data generation method using a multiscale entanglement renormalization network, enabling efficient creation and denoising of quantum states on NISQ processors with high success rates.

Contribution

It proposes a novel quantum denoising probability model based on MERA, demonstrating its effectiveness in generating and denoising quantum states like GHZ and W states with high success rates.

Findings

Success rate above 99% for generating GHZ-like and W-like states.

Near 100% success in denoising under certain noise conditions.

Effective denoising of multiple quantum data types simultaneously.

Abstract

Quantum technology has entered the era of noisy intermediate-scale quantum (NISQ) information processing. The technological revolution of machine learning represented by generative models heralds a great prospect of artificial intelligence, and the huge amount of data processes poses a big challenge to existing computers. The generation of large quantities of quantum data will be a challenge for quantum artificial intelligence. In this work, we present an efficient noise-resistant quantum data generation method that can be applied to various types of NISQ quantum processors, where the target quantum data belongs to a certain class and our proposal enables the generation of various quantum data belonging to the target class. Specifically, we propose a quantum denoising probability model (QDM) based on a multiscale entanglement renormalization network (MERA) for the generation of quantum data. To show the feasibility and practicality of our scheme, we demonstrate the generations of the classes of GHZ-like states and W-like states with a success rate above 99%. Our MREA QDM can also be used to denoise multiple types of quantum data simultaneously. We show the success rate of denoising both GHZ-like and W-like states with a single qubit noise environment of noise level within 1/4 can approximate to be 100%, and with two other types of noise environment with noise level within 1/4 can be above 90%. Our quantum data generation scheme provides new ideas and prospects for quantum generative models in the NISQ era.