Precise Image Generation on Current Noisy Quantum Computing Devices

TL;DR

This paper introduces the Quantum Angle Generator (QAG), a novel quantum machine learning model that generates highly accurate images on current noisy quantum devices, demonstrating robustness to hardware noise and calibration changes.

Contribution

The paper presents the first quantum model achieving high-precision image generation on NISQ devices, utilizing variational circuits and MERA architecture, with extensive noise robustness analysis.

Findings

QAG achieves state-of-the-art image accuracy on noisy quantum hardware.

The model learns and compensates for hardware noise characteristics.

It tolerates calibration changes up to 8% during training.

Abstract

The Quantum Angle Generator (QAG) is a new full Quantum Machine Learning model designed to generate accurate images on current Noise Intermediate Scale (NISQ) Quantum devices. Variational quantum circuits form the core of the QAG model, and various circuit architectures are evaluated. In combination with the so-called MERA-upsampling architecture, the QAG model achieves excellent results, which are analyzed and evaluated in detail. To our knowledge, this is the first time that a quantum model has achieved such accurate results. To explore the robustness of the model to noise, an extensive quantum noise study is performed. In this paper, it is demonstrated that the model trained on a physical quantum device learns the noise characteristics of the hardware and generates outstanding results. It is verified that even a quantum hardware machine calibration change during training of up to 8% can be well tolerated. For demonstration, the model is employed in indispensable simulations in high energy physics required to measure particle energies and, ultimately, to discover unknown particles at the Large Hadron Collider at CERN.