通过量子计算优化投资组合的最佳实践,在真实量子设备上进行实验
Giuseppe Buonaiuto1, Francesco Gargiulo1, Giuseppe De Pietro1
1Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), 80131, Naples, Italy.
Scientific reports
|November 8, 2023
概括
这项研究探讨了用于投资组合优化的量子计算,发现变量量子Eigensolver (VQE) 可以在量子硬件上实现适当的超参数调整,从而实现近乎精确的解决方案. 该研究确定了VQE的最佳设置,以提高金融投资组合管理的效率.
科学领域:
- 量子计算是一种量子计算.
- 计算金融是指计算金融.
- 优化算法 优化算法
背景情况:
- 投资组合优化面临着可扩展性挑战,随着市场尺寸的增加.
- 量子计算为克服金融领域的计算复杂性提供了一个潜在的解决方案.
- 经典的优化方法与大规模的,受约束的二次问题作斗争.
研究的目的:
- 解决投资组合优化问题使用变量量子Eigensolver (VQE).
- 在真实量子计算机上的投资组合优化中识别和定义VQE的最佳超参数.
- 评估VQE在金融应用中的性能和可扩展性.
主要方法:
- 制定受约束的二次级投资组合优化问题.
- 将问题转换为使用二进制编码的二进制不受约束的二进制优化 (QUBO).
- 将 QUBO 问题转换为用于量子计算的伊辛哈密尔顿式.
- 使用变量量子自身解决器 (VQE) 找到最小自身值 (最佳解决方案).
- 在模拟器和真实量子设备上尝试各种方法和优化方法.
主要成果:
- VQE的性能高度依赖于量子硬件的大小和超参数选择.
- 最佳的超参数选择使真实设备上的VQE能够达到接近精确的解决方案.
- 量子算法证明了对经典解决方案的强烈趋同,即使没有错误减轻.
- 解决方案质量与量子处理器维度相关,如不同量子设备所示.
- 该研究提供了基于VQE的投资组合优化最佳实践的证据.
结论:
- 变量量子Eigensolver (VQE) 是量子计算机上投资组合优化的可行和高效方法.
- 仔细选择超参数和足够的量子硬件对于获得高质量的结果至关重要.
- 量子计算,特别是VQE,作为硬件尺度,有望实现更高效的财务优化.
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