在一致的超旋转机器中进行超缩放
Marcello Calvanese Strinati1, Claudio Conti1,2
1Centro Ricerche Enrico Fermi (CREF), Via Panisperna 89a, 00184 Rome, Italy.
Physical review letters
|January 19, 2024
概括
我们开发了Ising哈密尔顿模拟的维度化,大大提高了大规模优化和机器学习任务的成功率. 这种方法提高了量子和经典Ising设备的性能.
科学领域:
- 计算物理 计算物理
- 量子计算是一种量子计算.
- 机器学习 机器学习
背景情况:
- 哈密尔顿式对优化和机器学习至关重要.
- 当前的量子和经典设备在可扩展性方面扎,随着系统大小的增加,其成功率下降.
研究的目的:
- 引入一种新的方法来提高Ising哈密尔顿模拟的性能和可扩展性.
- 为了抵消在大规模系统中观察到的成功率指数式下降.
主要方法:
- 伊辛哈密尔顿人的高维嵌入.
- 一种名为"维"的新技术,用于管理系统复杂性.
- 通过高性能计算中的融合动态进行验证.
主要成果:
- 在成功率和绩效指标方面取得了指数级的改进.
- 随着系统大小的增加,表现出性能减缓的下降.
- 使用高性能计算模拟验证了方法.
结论:
- 维度化为模拟Ising哈密尔顿式提供了显著的进步.
- 这种方法可以适应各种量子和经典的Ising设备,包括连贯的Ising机器.
- 超级缩放启发学提供了一个可通用的框架来提高Ising设备的性能.
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