Precise Image Generation on Current Noisy Quantum Computing Devices

RWTH Publications (RWTH Aachen)

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.

Authors 4

1. Florian Rehm Aachen

   European Organization for Nuclear Research · RWTH Aachen University

   Affiliation as printed

   CERN, Geneva, Switzerland

   RWTH Aachen University, Aachen, Germany

2. Sofia Vallecorsa

   European Organization for Nuclear Research

   Affiliation as printed

   CERN, Geneva, Switzerland

3. K. Borras Aachen

   Deutsches Elektronen-Synchrotron DESY · RWTH Aachen University

   Affiliation as printed

   Deutsches Elektronen-Synchrotron

   DESY, Hamburg, Germany

   RWTH Aachen University, Aachen, Germany

4. Valle Varo

   Deutsches Elektronen-Synchrotron DESY

   Affiliation as printed

   Deutsches Elektronen-Synchrotron

   DESY, Hamburg, Germany