Quantum channel learning with limited parallel access

Abstract:Quantum channels can characterized by their action on an orthogonal operator basis, where these operators are related to observable properties of the quantum system. For qudit and multimode bosonic systems, this is encoded respectively in the Heisenberg--Weyl transfer matrix estimated from the Choi state, and in the characteristic-function transfer function estimated from the Choi state generated by probing with a two-mode squeezed vacuum state. We derive sample-complexity bounds for estimating entries of these transfer matrix/function to additive accuracy $\epsilon$ with success probability $\geq1-\delta$, under different resources: access to the complex-conjugate channel $\mathcal{E}^*$ and/or simultaneous access to $c$ copies of the channel. In all settings, the learner uses parallel channel calls with adaptively chosen, ancilla-assisted input states and measurements. Absolute values of transfer-matrix entries can be learned efficiently with simultaneous access to $\mathcal{E}$ and $\mathcal{E}^*$, with tight scaling $\epsilon^{-4}$. Without conjugate access, any $c<d$ copies are insufficient for efficient learning, requiring sample complexity exponential in the number of ($d$-level) qudits $n$ (for prime $d$). Efficiency is recovered at $c=d$, with tight scaling $\epsilon^{-2d}$. For bosonic systems, exponential sample complexity persists for all $c=O(1/\epsilon)$. Although the task is learning a particular state, these bounds carry stronger implications than standard state-learning bounds since the learner controls the inputs and ancillary assistance. This establishes a hierarchy of channel-learning resources: self-complex-conjugate channels require two-copy ancilla-assisted access for efficient learning, while for every square-free $d$, some channels require $d$-copy access. As a corollary, we bounds tighter lower bounds for state learning with limited multi-copy access.

arXiv:2608.05307 [quant-ph]

(or arXiv:2608.05307v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2608.05307 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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