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This paper establishes near-optimal query bounds for incoherent tomography of low-rank quantum channels, resolving the efficiency gap between adaptive and nonadaptive protocols. By leveraging the Matrix Multiplicative Weight Update algorithm, the authors demonstrate that minimal input state adaptation suffices to achieve performance nearly matching theoretical lower limits. These results advance the fundamental understanding of resource requirements for characterizing quantum processes without memory retention.
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Near-optimal incoherent tomography of low-rank quantum channels

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arXiv:2609.23752v1 Announce Type: new Abstract: We study tomography for quantum channels with input dimension $d_1$, output dimension $d_2$, and Kraus rank at most $r$, to within diamond norm error $\varepsilon$, using adaptive experiments that retain no quantum memory between channel queries. - For quantum channels whose non-zero Choi eigenvalues are bounded below by $\Omega(d_1/r)$, we establish optimal query upper and lower bounds $\Theta(d_1d_2r^2/\epsilon^2)$. The upper bound is achieved by a nonadaptive algorithm that uses the estimator from [Surawy-Stepney et al., Quantum (2022)], together with a new diamond-norm analysis. The lower bound applies to arbitrary adaptive incoherent protocols and follows from a new local family of channels and a uniform one-query Fisher-information bound. - For general channels, we establish an upper bound $O(d_1d_2r^2\log(2d_1)/\epsilon^2)$, nearly matching the above lower bound $\Omega(d_1d_2r^2/\varepsilon^2)$. To achieve this, we generalize the above nonadaptive algorithm by adapting the input state over $O(\log(2d_1))$ rounds with the Matrix Multiplicative Weight Update algorithm.

Abstract:We study tomography for quantum channels with input dimension $d_1$, output dimension $d_2$, and Kraus rank at most $r$, to within diamond norm error $\varepsilon$, using adaptive experiments that retain no quantum memory between channel queries. - For quantum channels whose non-zero Choi eigenvalues are bounded below by $\Omega(d_1/r)$, we establish optimal query upper and lower bounds $\Theta(d_1d_2r^2/\epsilon^2)$. The upper bound is achieved by a nonadaptive algorithm that uses the estimator from [Surawy-Stepney et al., Quantum (2022)], together with a new diamond-norm analysis. The lower bound applies to arbitrary adaptive incoherent protocols and follows from a new local family of channels and a uniform one-query Fisher-information bound. - For general channels, we establish an upper bound $O(d_1d_2r^2\log(2d_1)/\epsilon^2)$, nearly matching the above lower bound $\Omega(d_1d_2r^2/\varepsilon^2)$. To achieve this, we generalize the above nonadaptive algorithm by adapting the input state over $O(\log(2d_1))$ rounds with the Matrix Multiplicative Weight Update algorithm.

arXiv:2609.23752 [quant-ph]

(or arXiv:2609.23752v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.23752 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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