Simulations of Quantum Approximate Optimization Algorithm on HPC-QC Integrated Systems

Conference · Wed Jan 01 00:00:00 EST 2025

DOI:https://doi.org/10.1109/QCE60285.2024.10357· OSTI ID:2538268

Kim, Seongmin ^[1]; Suh, In-Saeng ^[1]

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1. ORNL

The Quantum Approximate Optimization Algorithm (QAOA) has emerged as a promising tool for accelerating optimization processes in the Noisy Intermediate-Scale Quantum (NISQ) era. Compared to classical methods, QAOA efficiently solves optimization problems, often formulated as Quadratic Unconstrained Binary Optimization (QUBO) problems. Classical quantum simulators are crucial for evaluating quantum algorithms due to limited quantum resources. However, QAOA's performance can vary with different simulation methods. This study analyzes QAOA's performance using various quantum simulators (e.g., density _matrix, statevector, and matrix_product_state) and demonstrates the benefits of HPC-QC integrated systems in solving QUBO problems on an active learning workflow. By simulating QAOA on dense, large-matrix QUBO problems, we evaluate accuracy and problem-solving time. We also assess QAOA's performance on local computers and HPC-QC inte-grated systems, using Oak Ridge Leadership Computing Facility (OLCF)'s Frontier supercomputer with local Qiskit Aer and remote IBM Quantum simulators.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 2538268
• Resource Type: Conference paper/presentation
• Conference Information: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) - Montreal, Canada - 9/15/2024-9/20/2024
• Country of Publication: United States
• Language: English