Distributed Quantum Approximate Optimization Algorithm on Integrated High-Performance Computing and Quantum Computing Systems for Large-Scale Optimization

• URL: http://arxiv.org/abs/2407.20212v1
• Date: Mon, 29 Jul 2024 17:42:25 GMT
• Title: Distributed Quantum Approximate Optimization Algorithm on Integrated High-Performance Computing and Quantum Computing Systems for Large-Scale Optimization
• Authors: Seongmin Kim, Tengfei Luo, Eungkyu Lee, In-Saeng Suh,
• Abstract summary: Quantum approximated optimization algorithm (QAOA) has shown promise for solving optimization problems by providing quantum speedup on gate-based quantum computing systems. We propose a distributed QAOA (DQAOA), which leverages a high-performance computing-quantum computing integrated system. 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.
• Score: 1.7099366779394252
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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