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Tian-Yu Yang

Publications and source records attributed to Tian-Yu Yang.

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Speeding up the classical simulation of Gaussian boson sampling with limited connectivity

Gaussian Boson sampling (GBS) plays a crucially important role in demonstrating quantum advantage. As a major imperfection, the limited connectivity of the linear optical network weakens the quantum advantage result in recent experiments. Here we present a faster classical algorithm to simulate the GBS process with limited connectivity. In this work, we introduce an enhanced classical algorithm for simulating GBS processes with limited connectivity. It computes the loop Hafnian of an $n \times n$ symmetric matrix with bandwidth $w$ in $O(nw2^w)$ time which is better than the previous fastest algorithm which runs in $O(nw^2 2^w)$ time. This classical algorithm is helpful on clarifying how limited connectivity affects the computational complexity of GBS and tightening the boundary of quantum advantage in the GBS problem.

quant-ph

Post-selection in noisy Gaussian boson sampling: part is better than whole

Gaussian boson sampling is originally proposed to show quantum advantage with quantum linear optical elements. Recently, several experimental breakthroughs based on Gaussian boson sampling pointing to quantum computing supremacy have been presented. However, due to technical limitations, the outcomes of Gaussian boson sampling devices are influenced severely by photon loss. Here, we present an efficient and practical method to reduce the negative effect caused by photon loss. With no hardware modifications, our method takes the data post-selection process that discards low-quality data according to our criterion to improve the performance of the final computational results, say part is better than whole. As an example, we show that the post-selection method can turn a GBS experiment that would otherwise fail in a ``non-classical test" into one that can pass that test. Besides improving the robustness of computation results of current GBS devices, this photon loss mitigation method may also benefit the further development of GBS-based quantum algorithms.

quant-ph