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This research presents a deterministic classical algorithm capable of estimating output probabilities for constant-depth quantum circuits in polynomial time, significantly outperforming previous methods that required super-polynomial complexity. The breakthrough is particularly relevant for verifying the behavior of shallow quantum devices and understanding the computational boundaries where classical simulation remains efficient despite arbitrary gate connectivity. By reducing the runtime from $n^{O(\log n)}$ to $\mathrm{poly}(n, 1/\varepsilon)$, this work advances our ability to benchmark near-term quantum hardware without relying on probabilistic sampling or full-scale quantum resources.
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Polynomial-time additive-error estimation of output probabilities for shallow quantum circuits

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arXiv:2610.02146v1 Announce Type: new Abstract: We give a deterministic classical algorithm that estimates $|\langle x|U|0^n\rangle|^2$ to additive error $\varepsilon$ in $\mathrm{poly}(n, 1/\varepsilon)$ time, where $U$ is a constant-depth quantum circuit comprised of gates with bounded fan-in and arbitrary connectivity, and $x$ is an arbitrary $n$-bit output string. This improves over prior state-of-the-art algorithms that takes $n^{O(log(n))}$ time for the same task, $n^{O(log(log(n))}$ when $U$ is geometrically local, and $n^{O(1)}$ for 2D geometrically-local circuits.

Abstract:We give a deterministic classical algorithm that estimates $|\langle x|U|0^n\rangle|^2$ to additive error $\varepsilon$ in $\mathrm{poly}(n, 1/\varepsilon)$ time, where $U$ is a constant-depth quantum circuit comprised of gates with bounded fan-in and arbitrary connectivity, and $x$ is an arbitrary $n$-bit output string. This improves over prior state-of-the-art algorithms that takes $n^{O(log(n))}$ time for the same task, $n^{O(log(log(n))}$ when $U$ is geometrically local, and $n^{O(1)}$ for 2D geometrically-local circuits.

Quantum Physics (quant-ph); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS)

arXiv:2610.02146 [quant-ph]

(or arXiv:2610.02146v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.02146 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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