Standalone FPGA-Based QAOA Emulator for Weighted-MaxCut on Embedded Devices

• URL: http://arxiv.org/abs/2502.11316v1
• Date: Sun, 16 Feb 2025 23:30:16 GMT
• Title: Standalone FPGA-Based QAOA Emulator for Weighted-MaxCut on Embedded Devices
• Authors: Seonghyun Choi, Kyeongwon Lee, Jae-Jin Lee, Woojoo Lee,
• Abstract summary: This study introduces a compact, standalone FPGA-based QC emulator for embedded systems. The proposed design reduces time complexity from O(N2) to O(N) where N equals 2n for n qubits. The emulator achieved energy savings ranging from 1.53 times for two-qubit configurations to up to 852 times for nine-qubit configurations.
• Score: 3.384874651944418
• License:
• Abstract: Quantum computing QC emulation is crucial for advancing QC applications, especially given the scalability constraints of current devices. FPGA-based designs offer an efficient and scalable alternative to traditional large-scale platforms, but most are tightly integrated with high-performance systems, limiting their use in mobile and edge environments. This study introduces a compact, standalone FPGA-based QC emulator designed for embedded systems, leveraging the Quantum Approximate Optimization Algorithm (QAOA) to solve the Weighted-MaxCut problem. By restructuring QAOA operations for hardware compatibility, the proposed design reduces time complexity from O(N^2) to O(N), where N equals 2^n for n qubits. This reduction, coupled with a pipeline architecture, significantly minimizes resource consumption, enabling support for up to nine qubits on mid-tier FPGAs, roughly three times more than comparable designs. Additionally, the emulator achieved energy savings ranging from 1.53 times for two-qubit configurations to up to 852 times for nine-qubit configurations, compared to software-based QAOA on embedded processors. These results highlight the practical scalability and resource efficiency of the proposed design, providing a robust foundation for QC emulation in resource-constrained edge devices.

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