通过使用概率计算机在硬组合优化中推动量子优势的边界
Shuvro Chowdhury1, Navid Anjum Aadit2, Andrea Grimaldi3,4
1Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA 93106, USA. schowdhury@ucsb.edu.
Nature communications
|October 16, 2025
概括
使用蒙特卡洛算法的概率计算机为复杂的优化问题提供了可扩展的经典解决方案. 这些方法,包括自适应式并行炼,性能优于当前的量子化器,为量子优势建立了基线.
科学领域:
- 计算物理学的计算物理.
- 量子计算是一种量子计算.
- 优化算法的优化算法
背景情况:
- 量子计算显示出希望,但缺乏现实世界的优势.
- 需要经典方法来解决复杂的优化问题.
研究的目的:
- 呈现概率计算器作为一个可扩展的优化经典解决方案.
- 将经典算法与量子化器进行比较.
主要方法:
- 与蒙特卡洛算法硬件一起共同设计概率计算机.
- 实现离散时间模拟量子化和自适应平行化.
- 与3D旋转眼镜的领先量子炉进行基准测试.
主要成果:
- 模拟的量子化显示,随着复制数量的增加,扩展尺度得到了改进.
- 适应式并行化尺度有利,并且优于模拟量子化.
- 现场可编程门阵列 (FPGA) 和专用芯片加速算法并提高能源效率.
结论:
- 概率计算机为困难的优化问题提供了可扩展的经典途径.
- 为了评估实际的量子优势,建立了严格的经典基线.
- 适应式并行炼是应对现实世界的优化挑战的一个有前途的算法.
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