Physics-inspired Quantum Algorithms for Near-term Quantum computers

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT), grant 2025.07323.BDANA. Runs 2025-10-01 to 2029-09-30. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2026-06-26); national funding data, not CORDIS. Abstract given by the source: During the past few years we have witnessed an outstanding development in the scale and controllability of quantum devices, fueled by a large investment in the field of quantum technologies. We have already reached a stage where experimental works using superconducting qubits or linear-optical circuits can claim quantum computational advantage over the best supercomputers for some specific tasks. To further progress in the field of quantum computation over the next decade, when full-fledged quantum error correction is unlikely to be possible, a major challenge will be to design error-robust quantum algorithms for practical applications. In this project, this challenge will be tackled using physics-inspired concepts such as thermalization from quantum statistical physics or Hamiltonian dynamics of models of interacting qubits to design robust quantum search and amplitude amplification algorithms as well as tackle optimization problems using near-term quantum devices with limited coherence times.

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 2026-10-22. 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, funded by FCT, runs from 2025-10-01 to 2029-09-30, focusing on developing error-robust quantum algorithms using physics-inspired concepts. It aims to address challenges in quantum computation as full error correction remains unfeasible. Suggested technology pillar: computing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.