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

TL;DR

This paper presents a compact FPGA-based quantum emulator for the Weighted-MaxCut problem using QAOA, optimized for embedded devices, achieving significant resource and energy efficiency improvements over software solutions.

Contribution

It introduces a standalone FPGA emulator for QAOA on embedded systems, reducing complexity and resource use, enabling support for more qubits than existing designs.

Findings

Supports up to nine qubits on mid-tier FPGAs

Achieves 1.53x to 852x energy savings over software

Reduces time complexity from O(N^2) to O(N)

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.