通过张量网络对参数化量子电路进行协同预训练
Manuel S Rudolph1, Jacob Miller2, Danial Motlagh1
1Zapata Computing Canada Inc., 325 Front St W, Toronto, ON, M5V 2Y1, Canada.
Nature communications
|December 15, 2023
概括
本研究引入了参数化量子电路 (PQC) 的协同方法,通过将经典张量网络与量子资源相结合,减轻了荒的高原并提高了性能. 这种混合方法利用经典计算来指导量子优化,为实际的量子优势铺平了道路.
科学领域:
- 量子计算是一种量子计算.
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 参数化量子电路 (PQC) 是利用当前量子硬件的一个关键方法.
- 荒的高原和超越经典算法的挑战阻碍了PQC的发展.
- 高效优化PQC对于实现量子优势至关重要.
研究的目的:
- 引入一个协同框架,将PQC优化中的经典和量子资源结合起来.
- 解决和减轻PQC中荒漠高原和经典性能限制的问题.
- 通过混合方法展示一种实现实际量子优势的途径.
主要方法:
- 提出了一个协同作用的框架,将经典张量网络计算与PQC集成在一起.
- 经典资源被用来通过张量网络生成初始的高质量解决方案.
- 然后将这种经典输出转换为PQC以使用量子计算进一步精细化.
主要成果:
- 协同方法有效地缓解了PQC中的荒高原,用于高达100量子比特的系统.
- 性能随着古典或量子资源的应用增加而提高.
- 数字证据支持框架克服优化挑战的能力.
结论:
- 经典模拟方法可以成为实现量子优势的重要组成部分,而不仅仅是障碍.
- 协同方法为开发实际有用的量子算法提供了一个有希望的方向.
- 混合经典-量子策略对于推动量子计算领域的发展至关重要.
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