证明量子近似优化算法的缩放优势在一个经典难以解决的问题上
Ruslan Shaydulin1, Changhao Li1, Shouvanik Chakrabarti1
1Global Technology Applied Research, JPMorgan Chase, New York, NY 10017, USA.
Science advances
|May 29, 2024
概括
量子近似优化算法 (QAOA) 显示出解决复杂问题的前景. QAOA与量子最小发现相结合,为低自相对应的二进制序列问题提供了卓越的缩放.
科学领域:
- 量子计算是一种量子计算.
- 计算复杂性 计算复杂性
- 算法分析算法分析算法分析算法
背景情况:
- 量子近似优化算法 (QAOA) 是一个重要的优化量子算法.
- 它解决古典难以解决问题的能力尚未得到充分理解.
- 低自相关二进制序列 (LABS) 问题提出了一个重要的计算挑战.
研究的目的:
- 调查QAOA在经典难以解决的LABS问题上的表现.
- 为了比较QAOA的缩放与最先进的经典解决方案.
- 在量子硬件上的 LABS 执行 QAOA 的实验进展.
主要方法:
- 广泛的无噪声数值模拟高达40个量子比特的QAOA.
- 对于LABS问题,与分支和绑定的解决方案进行比较.
- 在带有错误检测的Quantinuum捕获离子处理器上进行QAOA的实验执行.
主要成果:
- 具有固定的参数的QAOA显示出比LABS的经典分支和绑定解决方案更好的运行时间缩放.
- 结合QAOA和量子最小值的发现,可以实现 LABS 问题的最佳实证缩放.
- 在被困离子量子处理器上的LABS中成功地实验QAOA的演示.
结论:
- QAOA显示了解决经典难以解决的优化问题的潜力.
- QAOA,特别是与量子最小发现相结合时,为量子加速提供了一个有前途的算法方法.
- 在被困离子处理器上的实验验证支持QAOA在实际量子计算中的实用性.
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