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  • Open Access

Classical Algorithms for Measurement-Adaptive Gaussian Circuits

Changhun Oh1,* and Youngrong Lim2

  • *Contact author: changhun0218@gmail.com
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PRX Quantum 7, 010329 – Published 11 February, 2026

DOI: https://doi.org/10.1103/rpl7-ylg8

Abstract

Gaussian building blocks are essential for photonic quantum information processing, and universality can be practically achieved by equipping Gaussian circuits with adaptive measurement and feedforward. The number of adaptive steps then provides a natural parameter for computational power. Rather than assessing power only through sampling problems—the usual benchmark—we follow the ongoing shift toward tasks of practical relevance and study the quantum mean-value problem, i.e., estimating observable expectation values that underpin simulation and variational algorithms. More specifically, we analyze bosonic circuits with adaptivity and prove that when the number of adaptive measurements is small, the mean-value problem admits efficient classical algorithms even if a large amount of non-Gaussian resources are present in the input state, whereas less constrained regimes are computationally hard. This yields a task-level contrast with sampling, where non-Gaussian ingredients alone often induce hardness, and provides a clean complexity boundary parameterized by the number of adaptive measurement-and-feedforward steps between classical simulability and quantum advantage. Beyond the main result, we introduce classical techniques—including a generalization of Gurvits’ second algorithm to arbitrary product inputs and Gaussian circuits—for computing the marginal quantities needed by our estimators, which may be of independent interest.

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Photonic quantum computing using measurement adaptivity is a leading candidate for demonstrating computational advantage, yet the exact boundary between classical simulability and quantum supremacy in this regime remains an open question. We address this by developing a classical algorithm capable of efficiently estimating the mean values of observables in measurement-adaptive Gaussian circuits. Crucially, our results distinguish this task from the more computationally demanding problem of sampling; we prove that regimes with limited adaptivity admit efficient classical simulation, even in the presence of non-Gaussian resources. This work refines our understanding of the necessary conditions for quantum advantage, providing essential theoretical tools for benchmarking and verifying the performance of future large-scale optical quantum processors.

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PRX Quantum 7, 010329– Published 11 February, 2026
  • Received 16 September 2025
  • Revised 30 November 2025
  • Accepted 6 January 2026
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