量子神经网络启发了硬件可适应的替代方案,用于有效的化学系统量子模拟
Xiongzhi Zeng1, Yi Fan2, Jie Liu3
1Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China.
Journal of chemical theory and computation
|December 4, 2023
概括
我们介绍了一个新的量子神经网络灵感的替代品,用于变量量子自身解决器. 这种可适应的替代品可以提高噪音中等尺度量子 (NISQ) 设备的量子资源利用率和噪音弹性.
科学领域:
- 量子计算是一种量子计算.
- 计算化学计算化学
- 量子算法 量子算法 量子算法
背景情况:
- 变量量子自溶解器 (VQE) 是解决施罗丁格方程的一个关键量子算法.
- VQE性能严重依赖于波函数的可表达性和效率.
- 杂的中级量子计算机 (NISQ) 由于有限的量子比特数量和连贯时间而带来了挑战.
研究的目的:
- 为VQE提出一种新的,硬件效率高的波函数替代品.
- 为了增强在不同的量子硬件上对Ansatz的表达性和适应性.
- 在NISQ计算中提高量子资源利用率和噪声弹性.
主要方法:
- 在量子神经网络架构的启发下开发了一种新方法.
- 通过改变电路深度和宽度来研究表达能力.
- 在超导量子计算机上使用辅助量子比特证明了电路深度减小.
主要成果:
- 拟议的ansatz的可表达性与电路深度或宽度相变,提供硬件适应性.
- 安西拉量子比特显著降低了电路深度,这是超导硬件的关键因素.
- 在实际应用中,安萨茨表现出更高的抗噪强度.
结论:
- 新的硬件启发式替代方案为NISQ设备上的VQE提供了灵活的框架.
- 安西拉量子位集成提高了效率和噪声弹性,这对于实际量子计算至关重要.
- 这项工作为量子计算在NISQ时代的更广泛应用铺平了道路.
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