Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling
Soumyadip Paul, Sarit Chakraborty · 2025
Quantum technology offers a transformative approach to solving complex computational challenges in decentralized systems, particularly in blockchain transaction scheduling. Efficient transaction scheduling is critical in distributed ledger systems, which often face challenges like dynamic transaction loads. This work proposes a novel approach namely Hybrid Quantum Approximate Optimization Algorithm (HQAOA) which combines Multi-Agent QAOA (MA-QAOA) and Adaptive Layer QAOA (AL-QAOA) to provide a robust solution to such issues. HQAOA leverages a decentralized optimization framework where multiple agents, each representing a node in the network, optimize local transaction scheduling while considering global constraints. Additionally, HQAOA adapts to varying transaction loads by dynamically adjusting the number of quantum layers for each agent, ensuring computational efficiency under different conditions. Such dynamic layer adjustment mechanisms mitigate the adverse effects of quantum noise and ensures optimal performance in scenarios with fluctuating transaction loads. In contrast to traditional QAOA, which struggles with noise and scaling issues, HQAOA provides improved performance by balancing the complexity of the problem with available quantum resources. Experimental results signify the future potential of HQAOA to outperform QAOA, particularly in noisy environments and irregular transaction conditions. This work demonstrates the potential of HQAOA to optimize blockchain transaction scheduling in decentralized systems.