Optimizing models of hybrid quantum/classical computation

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT), grant 2020.07245.BD. Runs 2021-01-01 to 2024-12-31. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2026-07-23); national funding data, not CORDIS. Abstract given by the source: Quantum computers offer the promise of computational speed-up for many problems. The current small prototypes, however, are still approaching the quantum computational advantage regime. This project will investigate different practical ways we can combine quantum and classical computers for enhanced computation, and how to quantify the resources used. Some key recent results are hybrid quantum/classical algorithms for variational ground-state energy estimation of Hamiltonians [1]; logical 3-satisfiability problems [2]; and Bayesian inference [3]. Our goal will be to refine these hybrid computation models, with a view to proposing practical solutions using current or near-term quantum devices: cloud-based quantum computers, and integrated photonic chips to be developed by collaborators. By turning theoretical proposals into practical, software-enabled solutions, we will bring closer the goal of combining classical computers and small-scale quantum devices for computational advantage.

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Fundação para a Ciência e a Tecnologia, I.P. (FCT). Retrieved 2026-10-28. Licence: Creative Commons Attribution 4.0 (OpenAIRE Graph). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: Creative Commons Attribution 4.0 (CC BY 4.0).

AI-assisted summary: The project "Optimizing models of hybrid quantum/classical computation" is funded by FCT and runs from January 2021 to December 2024. It aims to enhance computation by combining quantum and classical computers and refining hybrid computation models. Suggested technology pillar: computing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.