We study the number of copies needed to estimate the root Uhlmann fidelity between an unknown quantum state and a classically known reference, under collective measurements. If the unknown state has rank at most $s$, we give an estimator using $O(s^2/\varepsilon^2)$ copies, uniformly in the ambient dimension and reference rank. The estimator applies a random-purification channel and a covariant pure-state measurement, then rescales the observed amplitude before evaluating a weighted nuclear norm. Combined with the lower bound established independently in our first version and in concurrent work of Wang, this determines the worst-case complexity under rank bounds $r,s$ as $\min\{r,s\}^2/\varepsilon^2$ up to logarithmic factors in the smaller rank.
The same estimator gives the upper bound $O(r(\operatorname{tr}\sqrtσ)^2/\varepsilon^2)$ for a rank-$r$ reference $σ$. We combine spectral truncation with lower bounds obtained by embedding hard instances for the maximally mixed reference into spectral subspaces. For spectra $λ_i\propto i^{-α}$ with fixed $1<α\le2$, as $\varepsilon\downarrow0$ with $r\ge C_α\varepsilon^{-2/(α-1)}$, the bounds determine the complexity as $\widetildeΘ_α(\varepsilon^{-4/(α-1)})$. In particular, inverse-square spectra have accuracy exponent four. The lower-bound construction uses exact moment matching and an explicitly computable Schur measure.
We study the number of copies needed to estimate the root Uhlmann fidelity between an unknown quantum state and a classically known reference, under collective measurements. If the unknown state has rank at most s, we give an estimator using O(s2/ε2) copies, uniformly in the ambient dimension and reference rank. The estimator applies a random-purification channel and a covariant pure-state measurement, then rescales the observed amplitude before evaluating a weighted nuclear norm. Combined with the lower bound established independently in our first version and in concurrent work of Wang, this determines the worst-case complexity under rank bounds r,s as min{r,s}2/ε2 up to logarithmic factors in the smaller rank.
The same estimator gives the upper bound O(r(trσ)2/ε2) for a rank-r reference σ. We combine spectral truncation with lower bounds obtained by embedding hard instances for the maximally mixed reference into spectral subspaces. For spectra λi∝i−α with fixed 1<α≤2, as ε↓0 with r≥Cαε−2/(α−1), the bounds determine the complexity as Θα(ε−4/(α−1)). In particular, inverse-square spectra have accuracy exponent four. The lower-bound construction uses exact moment matching and an explicitly computable Schur measure.
The Uhlmann fidelity F(ρ0,ρ1)=tr∣ρ0ρ1∣ is one of the most fundamental quantities in quantum information theory for quantifying the closeness between two quantum states. Estimating the Uhlmann fidelity to within additive error ε requires a number of copies of the states, or queries to their state-preparation circuits, that depends at least linearly on the smaller of the ranks of ρ0 and ρ1. Consequently, this rank dependence disappears when either state is pure, in which case the query and sample complexities depend only polynomially on 1/ε. However, the known optimal estimator for F(ρ,∣ψ⟩⟨ψ∣) due to Fang and Wang (ESA 2025) requires prior knowledge of which state is pure.
In this work, we remove this mathematically unnecessary prior-knowledge requirement and establish an optimal estimator for F(ρ,∣ψ⟩⟨ψ∣) under the sole promise that one of the two states is pure, without knowing which one. Our estimator is obtained by specializing the refined algorithmic Uhlmann transform of Utsumi, Nakata, Wang, and Takagi (2025) to the case where one state is pure. In this setting, the Uhlmann fidelity can be recovered as follows: apply a unitary dilation of trA(∣ψ0⟩⟨ψ1∣) (or its inverse) to the reference register R of the purification ∣ψ1⟩ (or ∣ψ0⟩) on the registers A and R, estimate the corresponding square-root amplitude in each case, and take the maximum of the resulting two estimates.
We study coherent classical shadows protocols for an unknown d-dimensional quantum state of rank at most r, allowing an arbitrary joint measurement on all available copies. For observables with operator norm at most one and centered squared Frobenius norm at most F, we study the minumum number of copies T needed for the protocol to successed with propbability 1−δ. We prove a lower bound on T, that depends both on δ and the number M of post-measurement observables. Denoting ℓ=max{log(2M),log(1/δ)}, we show that T=Ω(min{εrFℓ,ε4/3(Fℓ)2/3}+ε2ℓ) under the mild assumption log(2M)≲dmin{r,F}. The lower bound nearly matches the known upper bound and matches the conjectural strengthening of the known upper bound.
The proof is inspired by the framework of the proof of the lower bound for Boolean Hidden Matching. We develop a Gaussian-lift noise kernel that transfers forward and reverse Gaussian hypercontractivity to the Grassmannian, allowing us to bound the distinguishing advantage in an auxiliary game with a revealed query observable. We use Schur--Weyl structure and posterior moment bounds to control arbitrary coherent measurements on the source side.
Quantum state cloning is the task of approximately producing additional copies of an unknown quantum state from a finite number of input copies. The optimal cloning fidelity is known exactly for pure states, but no comparable characterization is known for general mixed states. We determine the optimal asymptotic copy complexity for d-dimensional states of rank at most r: producing M additional copies with worst-case fidelity at least 1−ε requires and is achievable with N=Θ(Mrd/ε) input copies. Remarkably, the lower bound already holds for a family of states with a fixed flat spectrum, while the matching upper bound is achieved by random purification followed by optimal pure-state cloning. For M=1, we further show that high-fidelity tomography can be coherently converted into cloning with comparable error, revealing an operational origin of the matching cloning and tomography complexities.
We consider the problem of estimating the fidelity of an unknown quantum state to a known reference state to within additive error ε. We show that the sample complexity is O(r2/ε2) with optimal ε-dependence when the reference state is of rank r, improving the previous best O(r2log2(1/ε)/ε4) due to Utsumi, Nakata, Wang, and Takagi (QIP 2026). We also provide a lower bound of Ω(r/ε2), improving the previous best Ω(r/ε+1/ε2), with implications to quantum query complexity. Moreover, we further consider the case where the unknown state is of rank at most r while the reference state can be arbitrary, for which the sample complexity is shown to be O(r2/ε4). As an application, we present an approach to tolerant quantum state certification, generalizing the exact certification studied in Bădescu, O'Donnell, and Wright (STOC 2019).
We study quantum state tomography when the unknown state lies in a trace-distance ball of radius R around a known reference state ρ. For arbitrary collective measurements, we determine the minimax expected risk and confidence radius up to universal constants, uniformly over the dimension, center spectrum, radius, and sample size. Write Γ=d(Trρ)2+d2R. For d≥2, 0<R≤1, and n≥1 copies, the expected risk is Θ(min{R,Γ/n}). At failure probability 0<δ≤1/3, the minimax confidence radius is Θ(min{R,nΓ+log(1/δ)}). For ε≤c0R with a universal c0>0, the copy complexity is Θ((Γ+log(1/δ))/ε2). For the lower bound we use a weighted trace-norm witness to retain all spectral scales in a square-root perturbation family. For the upper bound, we give a direct spectral and concentration analysis of a modified existing purification-based procedure.
Optimal single-copy estimation of quantum state moments: why Tr(ρ3) and Tr(ρ4) are equally hard
Estimating nonlinear properties of an unknown quantum state with restrictive experimental accessibility is a fundamental problem in quantum learning. We study the sample complexity of estimating the state moments Tr(ρt), allowing arbitrary adaptive single-copy measurements. While purity estimation has optimal sample complexity Θ(d) at constant additive error, the optimal dimension dependence for higher moments has remained unclear. We resolve this question for every fixed integer t≥2: for any sufficiently small constant additive error, the optimal sample complexity is Θ(d1+⌈log2t⌉⌈log2t⌉) with constant success probability. The complexity thus follows a dyadic hierarchy: for each integer h≥1, all moment orders 2h−1<t≤2h share the same exponent. In particular, the third and fourth moments both require Θ(d2/3) copies. Our results also apply to multistate overlaps, weighted and partial-transpose moments, fixed-rank spectrum estimation, and distributed estimation of nonlinear quantities. We also establish an Ω(d1−1/t) lower bound for arbitrary nonadaptive single-copy protocols, matching the known upper bound and demonstrating an advantage from adaptivity for every t≥4. Our upper bound uses adaptive state filtering to reduce higher-order moment estimation to lower-order moment estimation in a smaller state space. For the lower bound, we directly compare moment-matched ensembles through a smooth interpolation, bypassing the maximally mixed state as an intermediate hypothesis and yielding matching bounds beyond d.