Quantum Many-Body Ground States via Digital Quantum Simulation

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT), grant 2020.08444.BD. Runs 2020-09-01 to 2023-08-31. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2025-07-19); national funding data, not CORDIS. Abstract given by the source: In this PhD project I shall explore the use of quantum processors to find the ground state of quantum many-body systems in condensed matter physics that are beyond the capacity of conventional hardware. Building upon the research carried out by Prof Rossier and myself over the past months [1], I will consider two distinct approaches: hybrid variational algorithms and adiabatic state preparation. On the former, ansätze suited for condensed matter systems will be developed, taking both hardware efficiency and physical motivation considerations into account. Regarding the latter, protocols to prepare ground states of interacting systems starting from non-interacting or mean-field states will be explored, attempting to minimize the computational resources required to satisfy the adiabatic condition. In both cases, the methods developed will be applied to canonical condensed matter models, particularly the Ising and Heisenberg models for spin systems, and the t-J and Hubbard models for fermionic systems.

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 "Quantum Many-Body Ground States via Digital Quantum Simulation" is funded by FCT and runs from September 2020 to August 2023. It focuses on using quantum processors to find ground states of quantum many-body systems in condensed matter physics. Suggested technology pillar: simulation. Generated by AI from the public source text; it may contain errors and the original source is authoritative.