与真机无异：量子计算的快速弱模拟
Just Like the Real Thing: Fast Weak Simulation of Quantum Computation
AI总结：
针对量子计算机规模与可用性受限、量子研发依赖经典模拟的问题，提出基于决策图量子态表示的弱模拟算法，可生成与无错量子机统计一致的输出，首次实现大规模物理量子机的模拟。
摘要：
量子计算机有望为传统计算机难以解决的问题带来显著的速度提升，但尽管近期取得进展，其在规模扩展和可用性方面仍存在局限。因此，量子软硬件的开发高度依赖在传统计算机上运行的模拟技术。多数此类方法采用强模拟，即显式计算量子态的振幅。然而，这类信息无法从物理量子计算机中直接观测到，因为量子测量会从由这些振幅定义的概率分布中生成随机样本。在本研究中，我们聚焦于弱模拟，其目标是生成与无误差量子计算机的输出在统计上无法区分的结果。我们基于决策图表征的量子态，开发了用于弱模拟的算法。我们将这些算法与使用态向量数组和前缀和二分搜索进行采样的方法进行了对比。实验验证首次表明，该方法能够模拟规模可观的物理量子计算机。
英文摘要：
Quantum computers promise significant speedups in solving problems intractable for conventional computers but, despite recent progress, remain limited in scaling and availability. Therefore, quantum software and hardware development heavily rely on simulation that runs on conventional computers. Most such approaches perform strong simulation in that they explicitly compute amplitudes of quantum states. However, such information is not directly observable from a physical quantum computer because quantum measurements produce random samples from probability distributions defined by those amplitudes. In this work, we focus on weak simulation that aims to produce outputs which are statistically indistinguishable from those of error-free quantum computers. We develop algorithms for weak simulation based on quantum state representation in terms of decision diagrams. We compare them to using state-vector arrays and binary search on prefix sums to perform sampling. Empirical validation shows, for the first time, that this enables mimicking of physical quantum computers of significant scale.
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