Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Simpósio Brasileiro de Computação e Comunicação Quânticas (SBCCQ)

Evaluating Parameter Transfer in FALQON Across Graph Families

Alisson dos Passos Fumaco, Marcos Vinicius Reballo, Fernando Augusto Caletti de Barros, Gabriel Fernandes Thomaz, Eduardo I. Duzzioni

Published Jul 19, 2026• 0 citations
Publisher

Abstract

We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors (n ∈ {8, 10, 12}) to 14-node recipients. Using 3-regular and Erdős–Rényi families, we show 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.

DOI
10.5753/sbccq.2026.23365

Related papers

Evaluating Parameter Transfer in FALQON Across Graph Families

Alisson dos Passos Fumaco, Marcos Vinicius Reballo, Fernando Augusto Caletti de Barros +2 more

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-pharXiv:2605.29917•May 29, 2026•0 citations

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

Gabriel Fernandes Thomaz, Eduarda Rodrigues Monteiro, Jerusa Marchi +3 more

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.ETCross-listedarXiv:2605.04253•May 7, 2026•0 citations

Improving the efficiency of QAOA using efficient parameter transfer initialization and targeted-single-layer regularized optimization with minimal performance degradation

Shubham Patel, Utkarsh Mishra

Quantum approximate optimization algorithm (QAOA) have promising applications in combinatorial optimization problems (COPs). We investigated the MaxCut problem in three different families of graphs using QAOA ansats with parameter transfer initialization followed by targeted single layer optimization. For 3 regular (3R), Erdos Renyi (ER), and Barabasi Albert (BA) graphs, the parameter transfer approach achieved mean approximation ratios of 0.9443 for targeted-single layer optimization as compared to 0.9551 of full optimization. It represents 98.88 percent optimal performance, with 8.06 times computational speedup in unweighted graphs. But, in weighted graph families, optimal performance is relatively low (less than 90 percent) for higher nodes graph, suggesting parameter transfer followed by targeted-single-layer optimization is not ideal for weighted graph families, however, we find that for some weighted families (weighted 3-regular) this approach works perfectly. In 8.92 percent test cases, targeted single layer optimization outperformed the full optimization, indicating that complex parameter landscape can trap full optimization in sub-optimal local minima. To mitigate this inconsistency, ridge (L2) regularization is used to smoothen the solution landscape, which helps the optimizer to find better optimum parameters during full optimization and reduces these inconsistent test cases from 8.92 percent to 3.81 percent. This work demonstrates that efficient parameter initialization and targeted-single-layer optimization can improve the efficiency of QAOA with minimal performance degradation.

quant-pharXiv:2601.15760•Jan 23, 2026•0 citations

Parameter Transferability in QAOA Under Noisy Conditions

Quinn Langfitt, Jose Falla, Ilya Safro +1 more

The quantum approximate optimization algorithm (QAOA) is widely recognized for its potential to deliver quantum advantage in combinatorial optimization tasks. As a hybrid classical-quantum algorithm, it achieves an approximate solution to the combinatorial optimization problem by preparing a specific quantum state via a classical optimization of the quantum circuit parameters. A key property of QAOA is the observed concentration and transferability of optimal parameters across distinct problem instances, linked to local graph characteristics such as subgraph types (lightcones) and node degrees (parity) [1]. However, quantum noise poses a critical challenge in the current NISQ era, hindering the performance of QAOA, particularly for large system sizes. In this follow-up study, we investigate the resilience of parameter transferability under noisy conditions. Leveraging zero-noise extrapolation (ZNE), we evaluate the transferability's robustness across various random graphs under different noise levels. Our analysis reveals that the transferability of optimal parameters from smaller ‘donor’ graphs to larger ‘acceptor’ graphs retains its effectiveness, even under substantial noise when combined with ZNE. Importantly, by illustrating that QAOA parameter transferability can still be exploited effectively among NISQ devices, we offer a pragmatic approach to enhance the algorithm's performance. Our research reinforces the promise of QAOA for large-scale applications under the practical challenges of quantum noise.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•1 citation

Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning

Michael Mancini, Shabnam Sodagari

Feedback-based adaptive quantum optimization (FALQON) is a promising approach for solving combinatorial problems on noisy intermediate-scale quantum (NISQ) devices, requiring only single circuit evaluations per layer. However, standard FALQON relies on fixed hyperparameters that severely limit convergence speed, requiring hundreds to thousands of layers for acceptable solutions. This paper proposes Optimal FALQON, an optimization-based formulation that treats the per-layer time step ($δ_k$) and scaling factor ($M_k$) as decision variables optimized via classical methods. We present a comprehensive empirical study on all 94 non-isomorphic 3-regular graphs with 12 vertices, comparing Optimal FALQON with standard FALQON and multiple QAOA variants. Results demonstrate statistically significant improvements in success probability, evaluation efficiency, and depth-normalized cost across the evaluated benchmarks. Furthermore, initializing QAOA with parameters from Optimal FALQON yields superior warm-start performance compared to fixed initialization.

quant-pharXiv:2605.08332•May 11, 2026•0 citations

Graph representation learning for parameter transferability in quantum approximate optimization algorithm

Jose Falla, Quinn Langfitt, Yuri Alexeev +1 more

Abstract The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. Optimal QAOA parameter concentration effects for special MaxCut problem instances have been observed, but a rigorous study of the subject is still lacking. Due to clustering of optimal QAOA parameters for MaxCut, successful parameter transferability between different MaxCut instances can be explained and predicted based on local properties of the graphs, including the type of subgraphs (lightcones) from which graphs are composed as well as the overall degree of nodes in the graph (parity). In this work, we apply five different graph embedding techniques to determine good donor candidates for parameter transferability, including parameter transferability between different classes of MaxCut instances. Using this technique, we effectively reduce the number of iterations required for parameter optimization, obtaining an approximate solution to the target problem with an order of magnitude speedup. This procedure also effectively removes the problem of encountering barren plateaus during the variational optimization of parameters. Additionally, our findings demonstrate that the transferred parameters maintain effectiveness when subjected to noise, supporting their use in real-world quantum applications. This work presents a framework for identifying classes of combinatorial optimization instances for which optimal donor candidates can be predicted such that QAOA can be substantially accelerated under both ideal and noisy conditions.

arXiv:2401.06655•Quantum Machine Intelligence•Jul 22, 2024•22 citations
We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors (n ∈ {8, 10, 12}) to 14-node recipients. Using 3-regular and Erdős–Rényi families, we show 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.
We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors (n∈{8,10,12}n \in \{8,10,12\}n∈{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.
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.
Quantum approximate optimization algorithm (QAOA) have promising applications in combinatorial optimization problems (COPs). We investigated the MaxCut problem in three different families of graphs using QAOA ansats with parameter transfer initialization followed by targeted single layer optimization. For 3 regular (3R), Erdos Renyi (ER), and Barabasi Albert (BA) graphs, the parameter transfer approach achieved mean approximation ratios of 0.9443 for targeted-single layer optimization as compared to 0.9551 of full optimization. It represents 98.88 percent optimal performance, with 8.06 times computational speedup in unweighted graphs. But, in weighted graph families, optimal performance is relatively low (less than 90 percent) for higher nodes graph, suggesting parameter transfer followed by targeted-single-layer optimization is not ideal for weighted graph families, however, we find that for some weighted families (weighted 3-regular) this approach works perfectly. In 8.92 percent test cases, targeted single layer optimization outperformed the full optimization, indicating that complex parameter landscape can trap full optimization in sub-optimal local minima. To mitigate this inconsistency, ridge (L2) regularization is used to smoothen the solution landscape, which helps the optimizer to find better optimum parameters during full optimization and reduces these inconsistent test cases from 8.92 percent to 3.81 percent. This work demonstrates that efficient parameter initialization and targeted-single-layer optimization can improve the efficiency of QAOA with minimal performance degradation.
The quantum approximate optimization algorithm (QAOA) is widely recognized for its potential to deliver quantum advantage in combinatorial optimization tasks. As a hybrid classical-quantum algorithm, it achieves an approximate solution to the combinatorial optimization problem by preparing a specific quantum state via a classical optimization of the quantum circuit parameters. A key property of QAOA is the observed concentration and transferability of optimal parameters across distinct problem instances, linked to local graph characteristics such as subgraph types (lightcones) and node degrees (parity) [1]. However, quantum noise poses a critical challenge in the current NISQ era, hindering the performance of QAOA, particularly for large system sizes. In this follow-up study, we investigate the resilience of parameter transferability under noisy conditions. Leveraging zero-noise extrapolation (ZNE), we evaluate the transferability's robustness across various random graphs under different noise levels. Our analysis reveals that the transferability of optimal parameters from smaller ‘donor’ graphs to larger ‘acceptor’ graphs retains its effectiveness, even under substantial noise when combined with ZNE. Importantly, by illustrating that QAOA parameter transferability can still be exploited effectively among NISQ devices, we offer a pragmatic approach to enhance the algorithm's performance. Our research reinforces the promise of QAOA for large-scale applications under the practical challenges of quantum noise.
Feedback-based adaptive quantum optimization (FALQON) is a promising approach for solving combinatorial problems on noisy intermediate-scale quantum (NISQ) devices, requiring only single circuit evaluations per layer. However, standard FALQON relies on fixed hyperparameters that severely limit convergence speed, requiring hundreds to thousands of layers for acceptable solutions. This paper proposes Optimal FALQON, an optimization-based formulation that treats the per-layer time step (δkδ_kδk​) and scaling factor (MkM_kMk​) as decision variables optimized via classical methods. We present a comprehensive empirical study on all 94 non-isomorphic 3-regular graphs with 12 vertices, comparing Optimal FALQON with standard FALQON and multiple QAOA variants. Results demonstrate statistically significant improvements in success probability, evaluation efficiency, and depth-normalized cost across the evaluated benchmarks. Furthermore, initializing QAOA with parameters from Optimal FALQON yields superior warm-start performance compared to fixed initialization.
Abstract The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. Optimal QAOA parameter concentration effects for special MaxCut problem instances have been observed, but a rigorous study of the subject is still lacking. Due to clustering of optimal QAOA parameters for MaxCut, successful parameter transferability between different MaxCut instances can be explained and predicted based on local properties of the graphs, including the type of subgraphs (lightcones) from which graphs are composed as well as the overall degree of nodes in the graph (parity). In this work, we apply five different graph embedding techniques to determine good donor candidates for parameter transferability, including parameter transferability between different classes of MaxCut instances. Using this technique, we effectively reduce the number of iterations required for parameter optimization, obtaining an approximate solution to the target problem with an order of magnitude speedup. This procedure also effectively removes the problem of encountering barren plateaus during the variational optimization of parameters. Additionally, our findings demonstrate that the transferred parameters maintain effectiveness when subjected to noise, supporting their use in real-world quantum applications. This work presents a framework for identifying classes of combinatorial optimization instances for which optimal donor candidates can be predicted such that QAOA can be substantially accelerated under both ideal and noisy conditions.