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

2024-11-07

arXiv.org

AI Summary

Existing quantum simulators face dual bottlenecks: hardware-based approaches suffer from low flexibility and lack fidelity evaluation mechanisms, while software-based (CPU/GPU) solutions exhibit poor energy efficiency and weak scalability. This paper proposes an energy-efficient wavefunction-based FPGA quantum simulator, implemented on a Xilinx ZCU102 platform. It introduces the first four-dimensional co-optimization framework: (1) efficient memory organization, (2) configurable quantum gate units (QGUs), (3) dynamic scheduling, and (4) multi-precision number representation support (16-/24-/32-bit fixed- and floating-point). Customized resource mapping across LUTs, FFs, BRAMs, and DSPs balances wavefunction evolution accuracy with fine-grained gate-level modeling capability. Experimental results show a peak power consumption of only 2.41 W; the 32-bit variant achieves MSE < 1e−6 and gate fidelity > 0.999. On benchmarks such as QFT, the simulator reduces energy-delay product by 9870× over CPU implementations.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Quantum computing has emerged as a powerful tool for solving complex computational problems, but access to real quantum hardware remains limited due to high costs and increasing demand for efficient quantum simulations. Unfortunately, software simulators on CPUs/GPUs such as Qiskit, ProjectQ, and Qsun offer flexibility and support for a large number of 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, lack of fidelity evaluation, and power consumption. 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, including 16-bit floating point, 32-bit floating point, 16-bit fixed point, 24-bit fixed point, and 32-bit fixed point, are implemented on the Xilinx ZCU102 FPGA, utilizing between 9,226 and 18,093 LUTs, 1,440 and 7,031 FFs, 344 and 464 BRAMs, and 14 and 88 DSPs and consuming a maximum power of 2.41W. Experimental results demonstrate high accuracy in normalized gate speed, fidelity, and mean square error, particularly with 32-bit fixed-point and floating-point versions, establishing FQsun's capability as a precise quantum emulator. Benchmarking on quantum algorithms such as Quantum Fourier Transform, Parameter-Shift Rule, and Random Quantum Circuits reveals that FQsun achieves superior power-delay product, outperforming traditional software simulators on powerful CPUs by up to 9,870 times.

Problem

• High power consumption in CPU/GPU quantum simulators.
• Limited flexibility in hardware-based quantum emulators.
• Lack of fidelity evaluation in existing quantum emulators.

Innovation

• Efficient memory organization for quantum emulation
• Configurable Quantum Gate Unit (QGU) for flexibility
• Optimized scheduling and multiple number precisions