Precise image generation on current noisy quantum computing devices

F. Rehm, S. Vallecorsa, K. Borras, D. Krücker, M. Grossi, V. Varo

Quantum Science and Technology Vol. 9, nº. 1, pp. 015009-1 - 015009-16

Resumen:

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.

Palabras Clave: Full Quantum Generative Model, Quantum Image Generation, Detailed Quantum Inference Evaluation, Quantum Noise Study, Quantum Circuit Entanglement Study, Quantum Hardware Training

Índice de impacto JCR y cuartil WoS: 6,700 - Q1 (2022)

Referencia DOI: https://doi.org/10.1088/2058-9565/ad0389

Publicado en papel: Enero 2024.

Publicado on-line: Octubre 2023.

Cita:
F. Rehm, S. Vallecorsa, K. Borras, D. Krücker, M. Grossi, V. Varo, Precise image generation on current noisy quantum computing devices. Quantum Science and Technology. Vol. 9, nº. 1, pp. 015009-1 - 015009-16, Enero 2024. [Online: Octubre 2023]