FQsun: A Configurable Wave Function-Based Quantum Emulator for Power-Efficient Quantum Simulations

TL;DR

FQsun tackles the high energy cost and limited fidelity of software quantum simulators by delivering an FPGA-based, wave-function–driven emulator with a configurable QGU, efficient memory, optimized scheduling, and multi-precision support. Implemented on a Xilinx ZCU102, it achieves high fidelity with low MSE across tasks like RQC, QFT, and PSR, while delivering superior power-delay product compared with CPU/GPU software. The architecture enables up to $17$ qubits with five precision modes, and exhibits strong performance versus existing hardware emulators and certain software packages, particularly in energy-efficient scenarios. This work demonstrates a practical path toward scalable, high-fidelity quantum emulation suitable for benchmarking, quantum machine learning workflows, and energy-constrained quantum research.

Abstract

Quantum computers are promising powerful computers for solving complex problems, but access to real quantum hardware remains limited due to high costs. Although the software simulators on CPUs/GPUs such as Qiskit, ProjectQ, and Qsun offer flexibility and support for many qubits, they struggle with high power consumption and limited processing speed, especially as qubit counts scale. Accordingly, quantum emulators implemented on dedicated hardware, such as FPGAs and analog circuits, offer a promising path for addressing energy efficiency concerns. However, existing studies on hardware-based emulators still face challenges in terms of limited flexibility and lack of fidelity evaluation. To overcome these gaps, we propose FQsun, a quantum emulator that enhances performance by integrating four key innovations: efficient memory organization, a configurable Quantum Gate Unit (QGU), optimized scheduling, and multiple number precisions. Five FQsun versions with different number precisions are implemented on the Xilinx ZCU102, consuming a maximum power of 2.41W. Experimental results demonstrate high fidelity, low mean square error, and high normalized gate speed, particularly with 32-bit versions, establishing FQsun's capability as a precise quantum emulator. Benchmarking on famous quantum algorithms reveals that FQsun achieves a superior power-delay product, outperforming software simulators on CPUs in the processing speed range.

Figures (11)

• Figure 1: Quantum circuit simulation model where it can divide into parameterized and fixed parts, denoted as $U(\bm{\theta})$ and $V$, respectively (both can be notated as $U^{(\bm{t})}$). (Inset left) An example of a parameterized part is the ZXZ layer. (Inset right) An example of a fixed part is the QFT circuit; each part inside consists of many basic gates.
• Figure 2: Overview architecture of our FQsun at the system-on-chip (Soc) level on FPGA.
• Figure 3: Memory organization of the proposed FQsun.
• Figure 4: Micro-architecture of QGU.
• Figure 5: Timing chart of FQsun operation using Ping/Pong Amplitude Memories.

Theorems & Definitions (2)

• Definition 1: Strong and weak simulation
• Definition 2: Gate set for strong simulation