Distributed Quantum Approximate Optimization Algorithm on Integrated High-Performance Computing and Quantum Computing Systems for Large-Scale Optimization
<|reference_start|>Distributed Quantum Approximate Optimization Algorithm on Integrated High-Performance Computing and Quantum Computing Systems for Large-Scale Optimization: Quantum approximated optimization algorithm (QAOA) has shown promise for solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges in optimizing variational parameters for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, which limit its scalability for real-world applications. In this study, we propose a distributed QAOA (DQAOA), which leverages a high-performance computing-quantum computing (HPC-QC) integrated system. DQAOA leverages distributed computing strategies to decompose a large job into smaller tasks, which are then processed on the HPC-QC system. The global solution is iteratively updated by aggregating sub-solutions obtained from DQAOA, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1,000-bit problem) achieving high accuracy (~99%) and short time-to-solution (~276 s). To apply this algorithm to material science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible with our strategies. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.<|reference_end|>
@article{kim2024distributed,
    title={Distributed Quantum Approximate Optimization Algorithm on Integrated
  High-Performance Computing and Quantum Computing Systems for Large-Scale
  Optimization},
    author={Seongmin Kim, Tengfei Luo, Eungkyu Lee, and In-Saeng Suh},
    journal={arXiv preprint arXiv:2407.20212},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20212},
    primaryClass={cs.DC cs.CE quant-ph}
}