一个应用到一个NP完整问题的随机实例的量子增值进化算法
E Farhi1, J Goldstone, S Gutmann
1Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. farhi@mit.edu
概括
量子辅助算法利用缓慢的哈密尔顿演变来进行计算. 对NP完全问题的测试表明,量子计算机在复杂的任务中表现优于经典计算机.
科学领域:
- 量子物理学的量子物理学
- 计算机科学 计算机科学
- 算法开发的发展算法.
背景情况:
- 如果管理的哈密尔顿式变化缓慢,量子系统自然会保持其基本状态.
- 这一原理被称为量子附加动态行为,构成了新型量子计算算法的基础.
研究的目的:
- 为了评估量子增益算法的有效性.
- 为了测试其在具有挑战性的NP完全问题的实例上的性能.
主要方法:
- 这项研究应用了一种量子辅助算法,对随机生成的NP完全问题的硬实例进行了应用.
- 对小规模示例进行了模拟.
主要成果:
- 量子辅助算法在测试实例上表现出成功的性能.
- 结果表明,在特定的计算问题上,与经典计算机相比,它具有潜在的优势.
结论:
- 量子辅助算法显示出解决复杂计算问题的前景.
- 这些发现提供了量子计算机在解决艰难的NP-complete问题方面超越经典计算机的潜力的证据,这取决于大型量子硬件的发展.
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