Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach
Senrui Chen, Weiyuan Gong, Sisi Zhou
TL;DR
The paper delivers an instance-optimal, non-asymptotic characterization of high-precision shadow tomography under few-copy measurements by expressing the fundamental sample complexity as $\tilde{Θ}(Γ_p/ε^2)$, where $Γ_p$ is defined via the inverse Fisher information and depends on the observables set $\{O_i\}$. It introduces a two-step approach—coarse tomography to locate a nearby reference state, followed by local, unbiased estimation within a finite neighborhood—achieving near-optimal performance with a modest logarithmic overhead, and shows that entangled measurements over $c$ copies offer at most a $Ω(1/c)$ improvement in the high-precision regime. The work establishes a precise link between quantum learning and metrology, shows the equivalence of oblivious and non-oblivious tasks for $p=∞$, and provides concrete bounds for Pauli/shadow tomography, including explicit thresholds and dependence on the observables through $Γ_p$ and its oblivious variant. These results offer finite-sample guarantees and a unified metrological perspective for shadow tomography, with practical implications for benchmarking and quantum sensing using limited-copy measurements.
Abstract
We study the sample complexity of shadow tomography in the high-precision regime under realistic measurement constraints. Given an unknown $d$-dimensional quantum state $ρ$ and a known set of observables $\{O_i\}_{i=1}^m$, the goal is to estimate expectation values $\{\mathrm{tr}(O_iρ)\}_{i=1}^m$ to accuracy $ε$ in $L_p$-norm, using possibly adaptive measurements that act on $O(\mathrm{polylog}(d))$ number of copies of $ρ$ at a time. We focus on the regime where $ε$ is below an instance-dependent threshold. Our main contribution is an instance-optimal characterization of the sample complexity as $\tildeΘ(Γ_p/ε^2)$, where $Γ_p$ is a function of $\{O_i\}_{i=1}^m$ defined via an optimization formula involving the inverse Fisher information matrix. Previously, tight bounds were known only in special cases, e.g. Pauli shadow tomography with $L_\infty$-norm error. Concretely, we first analyze a simpler oblivious variant where the goal is to estimate an observable of the form $\sum_{i=1}^m α_i O_i$ with $\|α\|_q = 1$ (where $q$ is dual to $p$) revealed after the measurement. For single-copy measurements, we obtain a sample complexity of $Θ(Γ^{\mathrm{ob}}_p/ε^2)$. We then show $\tildeΘ(Γ_p/ε^2)$ is necessary and sufficient for the original problem, with the lower bound applying to unbiased, bounded estimators. Our upper bounds rely on a two-step algorithm combining coarse tomography with local estimation. Notably, $Γ^{\mathrm{ob}}_\infty = Γ_\infty$. In both cases, allowing $c$-copy measurements improves the sample complexity by at most $Ω(1/c)$. Our results establish a quantitative correspondence between quantum learning and metrology, unifying asymptotic metrological limits with finite-sample learning guarantees.