Alisson dos Passos Fumaco

arXiv2026

Evaluating Parameter Transfer in FALQON Across Graph Families

We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors ($n \in \{8,10,12\}$) to 14-node recipients. Using 3-regular and Erdős-Rényi families, we show that transfer success is dictated by the recipient graph, not the donor. Transfer excels for dense recipients -- achieving high approximation ratios regardless of the donor -- but remains challenging in sparse cross-family cases. Crucially, performance is highly resilient to donor size, with 8-node donors matching larger instances. Thus, cheap small graphs can provide robust parameters for larger targets, significantly reducing the measurement overhead of the feedback loop.

quant-ph↗

arXiv2026

Second-Order FALQON Parameter Transfer for the Max-Cut Problem on 3-Regular Graphs

The Feedback-based Algorithm for Quantum Optimization (FALQON) offers a deterministic alternative to variational quantum algorithms by bypassing classical optimization loops. However, maintaining convergence on large problem instances often requires restricting the time step, necessitating quantum circuit depths that exceed Noisy Intermediate-Scale Quantum (NISQ) hardware capabilities. This paper investigates the parameter transferability of second-order FALQON applied to the Max-Cut problem on 3-regular graphs. Through numerical experiments evaluating quantum circuits up to 16 layers on graphs up to 24 nodes, we demonstrate a highly advantageous scaling behavior: transferring feedback parameters optimized on small instances to larger target graphs yields significantly higher approximation ratios than natively optimizing the parameters directly on the larger graphs. This performance advantage arises because parameters trained on smaller instances can safely adopt aggressively larger time steps. By offloading the expensive parameter discovery phase to small-scale instances, this transfer strategy simultaneously reduces computational overhead and enhances the approximation ratio, thereby bringing FALQON closer to practical viability on near-term quantum architectures.

cs.ET↗