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This research analyzes how phase noise in Gaussian boson sampling affects the computational hardness of photonic quantum simulators, finding that dephasing preserves entanglement entropy and resists classical simulation via tensor networks. The study suggests that demonstrating quantum advantage may be feasible without strict input-state phase stabilization, offering a practical pathway for near-term experiments.
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Input phase noise in Gaussian Boson sampling

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arXiv:2512.19596v2 Announce Type: replace Abstract: Gaussian boson sampling is an important protocol for testing the performance of photonic quantum simulators. As such, various noise sources have been investigated that degrade the operation of such devices. In this paper, we examine a situation with phase noise between different modes of the input state leading to dephasing of the system. This models the phase fluctuations which remain even when the mean phase is controlled. We aim to determine whether these phase-noisy input states still form a computationally difficult problem. To do this, we use Matrix Product Operators to model the system, a technique recently used to model boson sampling scenarios. Our investigation finds that the Entanglement entropy grows linearly with the number of input states even for noisy input states. This implies that, unlike boson loss, this form of experimentally relevant noise remains difficult to simulate with tensor networks and may allow for the demonstration of quantum advantage without the need for implementing the challenging task of input-state phase stabilisation.

Abstract:Gaussian boson sampling is an important protocol for testing the performance of photonic quantum simulators. As such, various noise sources have been investigated that degrade the operation of such devices. In this paper, we examine a situation with phase noise between different modes of the input state leading to dephasing of the system. This models the phase fluctuations which remain even when the mean phase is controlled. We aim to determine whether these phase-noisy input states still form a computationally difficult problem. To do this, we use Matrix Product Operators to model the system, a technique recently used to model boson sampling scenarios. Our investigation finds that the Entanglement entropy grows linearly with the number of input states even for noisy input states. This implies that, unlike boson loss, this form of experimentally relevant noise remains difficult to simulate with tensor networks and may allow for the demonstration of quantum advantage without the need for implementing the challenging task of input-state phase stabilisation.

arXiv:2512.19596 [quant-ph]

(or arXiv:2512.19596v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2512.19596 Focus to learn more arXiv-issued DOI via DataCite

Phys. Rev. A 114, 022203 (2026)

https://doi.org/10.1103/pddb-5z5q Focus to learn more DOI(s) linking to related resources

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