Investigating layer-selective transfer learning of quantum approximate optimization algorithm parameters for the Max-Cut problem
Year: 2025
Authors: Venturelli F.A., Das S., Caruso F.
Autors Affiliation: Univ Pompeu Fabra, Dept Engn, Barcelona, Spain; Univ Florence, Dept Phys & Astron, Via Sansone 1, I-50019 Sesto Fiorentino, Italy; Barcelona Supercomp Ctr, QUANTIC, Barcelona, Spain; UIB CSIC, Inst Cross Disciplinary Phys & Complex Syst IFISC, Campus Univ Illes Balears, Palma De Mallorca, Spain; Univ Florence, European Lab Nonlinear Spect LENS, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy; Ist Nazl Ott Consiglio Nazl Ric CNR INO, I-50019 Sesto Fiorentino, Italy.
Abstract: The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successful in solving combinatorial optimization problems (COPs). It has been observed that the optimal parameters obtained from one instance of a COP can be transferred to another instance, resulting in generally good solutions for the latter. In this work, we propose a refinement scheme in which only a subset of QAOA layers is optimized following parameter transfer, with a focus on the Max-Cut problem. Our motivation is to reduce the complexity of the loss landscape when optimizing all the layers of high-depth QAOA circuits, as well as to reduce the optimization time. We investigate the potential hierarchical roles of different layers and analyze how the approximation ratio scales with increasing problem size. Our findings indicate that the selective layer optimization scheme offers a favorable trade-off between solution quality and computational time, and can be more beneficial than full optimization at a lower optimization time.
Journal/Review: PHYSICAL REVIEW A
Volume: 112 (4) Pages from: 42428-1 to: 42428-10
More Information: This work was supported by the European Union’s Research and Innovation Programme Horizon Europe G.A. No. 101070546 (MUQUABIS) , by the European Defence Agency under the project Q -LAMPS Contract No. B PRJ- RT-989, by the PNRR MUR project PE0000023-NQSTI, a nd by the MUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando 2022, Project No. 20227HSE83 ThAI-MIA funded by the European Union-Next Generation EU.DOI: 10.1103/cjjm-87gl
