Hybrid Quantum-Classical Algorithms: A Cloud On-Demand Viewpoint

One can download the paper from the publisher. There isn’t a pre-print available, but material has been re-used and updated from the EMU-TN on the cloud paper.

The work was presented at QUEST-IS’25 in the session “Quantum Algorithms, Computing & Simulation – Quantum Machine Learning A”.

The paper abstract is:

In the last decade, advances in quantum technologies have allowed for the rapid development of industrialized quantum processing units. These new devices exploit the laws of quantum mechanics to perform complex calculations. Quantum processing units require new ways of thinking and programming. In particular, these new algorithms will be hybrid, with part of the computation performed on classical high-performance computing hardware and part on the dedicated quantum hardware. At Pasqal, we have developed a cloud platform hosting a neutral atom quantum processing unit (QPU) operating in the analog paradigm and a series of hybrid quantum classical algorithms that cover applications such as quantum optimization, quantum machine learning and quantum simulation. In this paper, we will show how this platform is used during the execution of real workloads.