The state frame potential is a standard diagnostic of how closely a quantum state ensemble approximates Haar randomness. In this work, we study the problem of estimating the state frame potential of order $t$ to within additive error $\varepsilon$ under three progressively weaker access models: (i) query access to a multi-state-preparation oracle, (ii) general sample access, and (iii) single-copy sample access. In the query model, we establish a near-optimal query complexity of $\widetildeΘ(\sqrt{t}/\varepsilon)$, yielding a quadratic improvement in the dependence on $t$ over the previous best result of Nakata, Takeuchi, Kliesch, and Darmawan (PRX Quantum 2025). In the general sample model, we establish the optimal sample complexity $Θ(t/\varepsilon^2)$. In the single-copy sample model, we present a store-and-estimate approach whose sample complexity depends on the Rényi entropy of the ensemble weights. As an application, we use the single-copy algorithm to assess the randomness of projected state ensembles, where the entropy term becomes the observational Rényi entropy associated with measuring one subsystem.
The state frame potential is a standard diagnostic of how closely a quantum state ensemble approximates Haar randomness. In this work, we study the problem of estimating the state frame potential of order t to within additive error ε under three progressively weaker access models: (i) query access to a multi-state-preparation oracle, (ii) general sample access, and (iii) single-copy sample access. In the query model, we establish a near-optimal query complexity of Θ(t/ε), yielding a quadratic improvement in the dependence on t over the previous best result of Nakata, Takeuchi, Kliesch, and Darmawan (PRX Quantum 2025). In the general sample model, we establish the optimal sample complexity Θ(t/ε2). In the single-copy sample model, we present a store-and-estimate approach whose sample complexity depends on the Rényi entropy of the ensemble weights. As an application, we use the single-copy algorithm to assess the randomness of projected state ensembles, where the entropy term becomes the observational Rényi entropy associated with measuring one subsystem.
Entropy estimation of discrete probability distributions is a fundamental problem in statistical inference and quantum property estimation. In this paper, we study the problem of estimating Rényi entropy Hα(p) for any distribution p=(pi)i=1n given its purified quantum query-access. For non-integer α>1, our algorithm estimates Hα(p) to within additive error ε using only O(nmax{1/2,1−1/α}/ε) queries, which improves the best known upper bound for dependence on both n and 1/ε. For 0<α<1, our algorithm has query complexity O(n1/(2α)/εmax{1,1/(2α)}), which improves the dependence on 1/ε of the current best upper bound without incresing its n dependence. Our approach adapts the multi-level estimation framework to relative approximation of the power sums and leverage binary search to remove the requirement of prior knowledge of them. We also strengthen the precision-dependent lower bound to Ω(1/εmax{1,1/(2α)}) for 0<α<1, which identifies the optimal precision dependence of our algorithm.
Entropy is a measure of the randomness of a system. Estimating the entropy of a quantum state is a basic problem in quantum information. In this paper, we introduce a time-efficient quantum approach to estimating the von Neumann entropy S(ρ) and Rényi entropy Sα(ρ) of an N-dimensional quantum state ρ, given access to independent samples of ρ. Specifically, we provide the following: 1. A quantum estimator for S(ρ) with time complexity O~(N2), improving the prior best time complexity O~(N6) by Acharya, Issa, Shende, and Wagner (2020) and Bavarian, Mehraba, and Wright (2016). 2. A quantum estimator for Sα(ρ) with time complexity O~(N4/α−2) for 0<α<1 and O~(N4−2/α) for α>1, improving the prior best time complexity O~(N6/α) for 0<α<1 and O~(N6) for α>1 by Acharya, Issa, Shende, and Wagner (2020), though at a cost of a slightly larger sample complexity. Moreover, these estimators are naturally extensible to the low-rank case. We also provide a sample lower bound for estimating Sα(ρ). Technically, our method is quite different from the previous ones that are based on weak Schur sampling and Young diagrams. At the heart of our construction, is a novel tool called samplizer, which can "samplize" a quantum query algorithm to a quantum algorithm with similar behavior using only samples of quantum states; this suggests a unified framework for estimating quantum entropies. Specifically, when a quantum oracle U block-encodes a mixed quantum state ρ, any quantum query algorithm using Q queries to U can be samplized to a δ-close (in the diamond norm) quantum algorithm using Θ~(Q2/δ) samples of ρ. Moreover, this samplization is proven to be optimal, up to a polylogarithmic factor.
The entropy of a quantum system is a measure of its randomness, and has applications in measuring quantum entanglement. We study the problem of estimating the von Neumann entropy, S(ρ), and Rényi entropy, Sα(ρ) of an unknown mixed quantum state ρ in d dimensions, given access to independent copies of ρ. We provide algorithms with copy complexity O(d2/α) for estimating Sα(ρ) for α2) for estimating S(ρ), and Sα(ρ) for non-integral α > 1. These bounds are at least quadratic in d, which is the order dependence on the number of copies required for estimating the entire state ρ. For integral α > 1, on the other hand, we provide an algorithm for estimating Sα(ρ) with a sub-quadratic copy complexity of O(d2-2/α), and we show the optimality of the algorithms by providing a matching lower bound.
In this paper, we provide estimators for quantum Rényi and Tsallis entropies with nearly optimal sample complexity. Specifically, for order α, dimension d, and additive error ε,
1. For 0<α<1, the sample complexity is O(d1+1/α/ε1/α+d1/α−1/ε2) for Rényi entropy and O(d1+1/α/ε1/α+d2−2α/ε2) for Tsallis entropy. In particular, for 0<α≤1/2, the sample complexity for both entropies is O(d1+1/α/ε1/α).
2. For non-integer α>1, the sample complexity is O(d2/ε1/α+d1−1/α/ε2) for Rényi entropy.
Our upper bounds improve the quantum Rényi entropy estimators due to Acharya, Issa, Shende, and Wagner (2017) and the quantum Tsallis entropy estimators due to Chen, Liu, and Wang (2026), and match the lower bounds recently established by Wang (2026).
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).
Entropy is a measure of the randomness of a system. Estimating the entropy of a quantum state is a basic problem in quantum information. In this paper, we introduce a time-efficient quantum approach to estimating the von Neumann entropyS(ρ) and Rényi entropySα(ρ) of anN-dimensional quantum state ρ, given access to independent samples of ρ. Specifically, we provide the following quantum estimators. • A quantum estimator forS(ρ) with time complexity Õ (N2),1improving the prior best time complexity Õ(N6) by Acharya, Issa, Shende, and Wagner (2020) and Bavarian, Mehraba, and Wright (2016). • A quantum estimator forSα(ρ) with time complexity Õ (N4/α−2) for 0N4−2/α) for α > 1, improving the prior best time complexity Õ(N6/α) for 0N6) for α > 1 by Acharya, Issa, Shende, and Wagner (2020), though at a cost of a slightly larger sample complexity. Moreover, these estimators are naturally extensible to the low-rank case. We also provide a sample lower bound Ω(max{N/ε,N1/α−1/ε1/α}) for estimatingSα(ρ). Technically, our method is quite different from the previous ones that are based on weak Schur sampling and Young diagrams. At the heart of our construction, is a novel tool calledsamplizer, which can “samplize” a quantum query algorithm to a quantum algorithm with similar behavior using only samples of quantum states; this suggests a new framework for estimating quantum entropies. Specifically, when a quantum oracleUblockencodes a mixed quantum state ρ, any quantum query algorithm usingQqueries toUcan be samplized to a δ-close (in the diamond norm) quantum algorithm using Θ(Q2/δ) samples of ρ. Moreover, this samplization is proven to be optimal, up to a polylogarithmic factor.