We study the problem of testing identity of a collection of unknown quantum states given sample access to this collection, each state appearing with some known probability. We show that for a collection of d -dimensional quantum states of cardinality N , the sample complexity is O(Nd/ϵ2) , with a matching lower bound, up to a multiplicative constant. The test is obtained by estimating the mean squared Hilbert-Schmidt distance between the states, thanks to a suitable generalization of the estimator of the Hilbert-Schmidt distance between two unknown states by Bădescu, O'Donnell, and Wright \cite{Badescu2019}.
We study the problem of testing identity of a collection of unknown quantum states given sample access to this collection, each state appearing with some known probability. We show that for a collection of d -dimensional quantum states of cardinality N , the sample complexity is O(Nd/ϵ2) , with a matching lower bound, up to a multiplicative constant. The test is obtained by estimating the mean squared Hilbert-Schmidt distance between the states, thanks to a suitable generalization of the estimator of the Hilbert-Schmidt distance between two unknown states by Bădescu, O'Donnell, and Wright \cite{Badescu2019}.
We study the following quantum state identity problem: given access to copies of n unknown pure quantum states, the goal is to determine whether every pair has fidelity at least 1−ε or some pair has fidelity at most ε. This problem relaxes the identical-or-orthogonal promise considered in previous work. For any fixed overlap parameter ε∈(0,1/4), we present two quantum algorithms with complementary guarantees on circuit depth and sample complexity. The first runs in constant depth using parallel swap tests and takes O(log(n/δ)log(2/δ)) copies of each input state to succeed with probability at least 1−δ. The second uses the Schur transform to reduce the sample complexity to O(log(n/δ)), through an analysis relating Jucys--Murphy elements to Schur sampling. We also prove a matching sample complexity lower bound of Ω(log(n/δ)) for any quantum algorithm with failure probability at most δ≤1/2−c, for any constant c>0, establishing that the second algorithm achieves the optimal dependence on both n and δ for fixed ε.
We develop a unified framework for analyzing the complexity of quantum property testing through functionals of the form Lφ(ρ)=tr(φ(dρ))/d, where ρ is an unknown d-dimensional quantum state and φ is a given function. A master theorem is established that derives sample complexity lower bounds for estimating Lφ(ρ) from properties of φ, combining Haar-random moment encoding with moment matching and best polynomial approximation. Corresponding query complexity lower bounds follow from quantum sample-to-query lifting.
The framework yields (nearly) tight bounds for a broad class of problems, including entropy estimation (von Neumann, Rényi, and Tsallis), closeness estimation (trace distance and Uhlmann fidelity), spectrum estimation, rank testing (operator rank, Schmidt rank, and matrix product states). In particular, combined with known upper bounds, we show that the sample complexity of von Neumann entropy estimation is Θ(εlog2(d)max{1,εlog2(d)}d2+ε2log2(d)), and the sample complexity of spectrum estimation is Θ(ε2log2(d)max{1,ε2log2(d)}d2). In summary, our results resolve several open problems and establish the optimality of 37 quantum algorithms since 2015, up to constant or polylogarithmic factors.
This paper investigates symmetric composite binary quantum hypothesis testing (QHT), where the goal is to determine which of two uncertainty sets contains an unknown quantum state. While asymptotic error exponents for this problem are well-studied, the finite-sample regime remains poorly understood. We bridge this gap by characterizing the sample complexity -- the minimum number of state copies required to achieve a target error level. Specifically, we derive lower bounds that generalize the sample complexity of simple QHT and introduce new upper bounds for various uncertainty sets, including of both finite and infinite cardinalities. Notably, our upper and lower bounds match up to universal constants, providing a tight characterization of the sample complexity. Finally, we extend our analysis to the differentially private setting, establishing the sample complexity for privacy-preserving composite QHT.
Quantum sample-to-query lifting, a relation between quantum sample complexity and quantum query complexity presented in Wang and Zhang (SIAM J. Comput. 2025), was significantly strengthened by Tang, Wright, and Zhandry (2025) to the case of state-preparation oracles. In this paper, we compile a list of quantum lower and upper bounds for property testing that are obtained by quantum sample-to-query lifting. The problems of interest include testing properties of probability distributions and quantum states, such as entropy and closeness. This collection contains new results, as well as new proofs of known bounds. In total, we present 49 complexity bounds, where 41 are new and 18 are (near-)optimal.
Tight bounds on quantum sample complexity and quantum query complexity have been known for various computational problems in the literature, whereas tight bounds on quantum time complexity (i.e., the size of quantum circuits) remain unresolved. In this paper, we provide a framework to establish lower bounds on the quantum time complexity for testing permutation-invariant properties of quantum states, via a reduction from quantum sample complexity. As an application, we obtain a series of matching lower bounds when given sample access to the input quantum states, including:
1. The SWAP test due to Buhrman, Cleve, Watrous, and de Wolf (Phys. Rev. Lett. 2001) is time-optimal to estimate the purity tr(ρ2) and the inner product tr(ρσ).
2. The Shift test due to Ekert, Alves, Oi, Horodecki, Horodecki, and Kwek (Phys. Rev. Lett. 2002) is time-optimal to estimate the high-order functionals tr(ρk).
3. The productness tester for multipartite pure states due to Harrow and Montanaro (J. ACM 2013) is time-optimal.
4. The LMR protocol due to Lloyd, Mohseni, and Rebentrost (Nat. Phys. 2014) is time-optimal to implement the reflection operator about a pure state.
5. The samplizer due to Wang and Zhang (IEEE Trans. Inf. Theory 2025) is time-optimal for pure states.
6. The estimator for pure-state trace distance and fidelity due to Wang and Zhang (ICALP 2026) is time-optimal.
To the best of our knowledge, this is the first method that allows us to systematically establish tight lower bounds on quantum time complexity.
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).