Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1908.05251 · Physical Review A

Quantum algorithm for estimating α -Renyi entropies of quantum states

Sathyawageeswar Subramanian, Min-Hsiu Hsieh

Published Aug 24, 2021• 46 citations
arXivHTMLPDF

Abstract

We describe a quantum algorithm to estimate the $\ensuremath{\alpha}$-Renyi entropy of an unknown density matrix $\ensuremath{\rho}\ensuremath{\in}{\mathbb{C}}^{d\ifmmode\times\else\texttimes\fi{}d}$ for $\ensuremath{\alpha}\ensuremath{\ne}1$ by combining the recent technique of quantum singular value transformations with the method of estimating normalized traces in the one clean qubit model. We consider an oracular input model where the input state is prepared via a quantum oracle that outputs a purified version of the state, assumed to be nonsingular. Our method outputs an estimate of the $\ensuremath{\alpha}$-Renyi entropy to additive precision $\ensuremath{\epsilon}$, using an expected total number $O(\frac{1}{{(x\ensuremath{\epsilon})}^{2}})$ of independent applications of a quantum circuit which coherently queries the input unitary $O(\frac{1}{\ensuremath{\delta}}log\frac{d}{\ensuremath{\epsilon}})$ times, in each case measuring a single output qubit. Here $\ensuremath{\delta}$ is a lower cutoff on the smallest eigenvalue of $\ensuremath{\rho}$ and $x=\frac{1}{d}\mathrm{Tr}({\ensuremath{\rho}}^{\ensuremath{\alpha}})$. The expected number of measurements made in this method can be compared to results in the sample complexity model that generally require $\mathrm{\ensuremath{\Theta}}(\frac{{d}^{2}}{{\ensuremath{\epsilon}}^{2}})$ samples. Furthermore, we also show that multiplicative approximations can be obtained by iteratively using additive approximations, with an overhead logarithmic in the dimension $d$.

DOI
10.1103/physreva.104.022428

Related papers

Quantum algorithm for estimating α -Renyi entropies of quantum states

S Subramanian, Min-Hsiu Hsieh

We describe a quantum algorithm to estimate the α-Renyi entropy of an unknown density matrix ρ∈Cd×d for α≠1 by combining the recent technique of quantum singular value transformations with the method of estimating normalized traces in the one clean qubit model. We consider an oracular input model where the input state is prepared via a quantum oracle that outputs a purified version of the state, assumed to be nonsingular. Our method outputs an estimate of the α-Renyi entropy to additive precision ϵ, using an expected total number O(1(xϵ)2) of independent applications of a quantum circuit which coherently queries the input unitary O(1δlogdϵ) times, in each case measuring a single output qubit. Here δ is a lower cutoff on the smallest eigenvalue of ρ and x=1dTr(ρα). The expected number of measurements made in this method can be compared to results in the sample complexity model that generally require Θ(d2ϵ2) samples. Furthermore, we also show that multiplicative approximations can be obtained by iteratively using additive approximations, with an overhead logarithmic in the dimension d.

•Aug 1, 2021•0 citations

A Quantum Algorithm Framework for Discrete Probability Distributions With Applications to Rényi Entropy Estimation

Xinzhao Wang, Shengyu Zhang, Tongyang Li

Estimating statistical properties is fundamental in statistics and computer science. In this paper, we propose a unified quantum algorithm framework for estimating properties of discrete probability distributions, with estimating Rényi entropies as specific examples. In particular, given a quantum oracle that prepares ann-dimensional quantum state Σni=1√pi|i⟩, for α > 1 and 0Hα(p) to within additive error ϵ with probability at least 2/3 using Õ(n1-1/2α/ϵ + √n/ϵ1+ 1/2α) and Õ(n1/2α/ϵ1+ 1/2α) queries, respectively. This improves the best known dependence in ϵ as well as the joint dependence betweennand 1/ϵ. Technically, our quantum algorithms combine quantum singular value transformation, quantum annealing, and variable-time amplitude estimation. We believe that our algorithm framework is of general interest and has wide applications.

arXiv:2212.01571•IEEE Transactions on Information Theory•Mar 26, 2024•7 citations

Quantum Query Complexity of Entropy Estimation

Tongyang Li, Xiaodi Wu

Estimation of Shannon and Rényi entropies of unknown discrete distributions is a fundamental problem in statistical property testing. In this paper, we give the first quantum algorithms for estimating α-Rényi entropies (Shannon entropy being 1-Rényi entropy). In particular, we demonstrate a quadratic quantum speedup for Shannon entropy estimation and a generic quantum speedup for α-Rényi entropy estimation for all α ≥ 0 values, including tight bounds for the Shannon entropy, the Hartley entropy (α = 0), and the collision entropy (α = 2). We also provide quantum upper bounds for estimating min-entropy (α = +∞) as well as the Kullback-Leibler divergence. We complement our results with quantum lower bounds on α-Rényi entropy estimation for all α ≥ 0 values. Our approach is inspired by the pioneering work of Bravyi, Harrow, and Hassidim (BHH); however, with many new technical ingredients: 1) we improve the error dependence of the BHH framework by a fine-tuned error analysis together with Montanaro's approach to estimating the expected output of quantum subroutines for α = 0, 1; 2) we develop a procedure, similar to cooling schedules in simulated annealing, for general α ≥ 0, and 3) in the cases of integer α ≥ 2 and α = +∞, we reduce the entropy estimation problem to the α-distinctness and [log n]-distinctness problems, respectively.

IEEE Transactions on Information Theory•Nov 26, 2018•43 citations

Improved Quantum Algorithms for Rényi Entropies Estimation

Zongxing Xiong, Chengshen Gao, Lvzhou Li

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 $H_α(\mathbf{p})$ for any distribution $\mathbf{p} = (p_i)_{i=1}^n$ given its purified quantum query-access. For non-integer $α> 1$, our algorithm estimates $H_α(\mathbf{p})$ to within additive error $ε$ using only $\widetilde{\mathcal{O}}\big(n^{\max\{1/2,1-1/α\}}/ε\big)$ 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 $\widetilde{\mathcal{O}}\big(n^{1/(2α)}/ε^{\max\{1,1/(2α)\}}\big)$, 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 $Ω\big(1/ε^{\max\{1,1/(2α)\}}\big)$ for $0<α<1$, which identifies the optimal precision dependence of our algorithm.

quant-pharXiv:2610.19936•Oct 15, 2026•0 citations

Breaking the Multiplicative Overhead in Quantum Entropy Estimation

Junxiang Huang, Chenyang Li, Lu-Fan Zhang +2 more

The von Neumann, Tsallis, and Rényi entropies are fundamental measures of quantum information. A common estimation strategy multiplies the worst-case costs of spectral transformation and statistical readout, leaving gaps to query lower bounds. We reduce this overhead with multi-level algorithms that use local normalization and precision allocation, while variable-time estimation accounts for the probability of reaching expensive spectral tests. With controlled purified access and its inverse, we obtain bounds for additive error $\varepsilon$, rank upper bound $R$, and fixed order $α$. Tsallis entropy estimation has near-optimal rank-independent query complexity $\widetilde O(\varepsilon^{-1/(α-1)})$ for $1<α<2$, when the allowed dimension and rank accommodate the lower-bound instances, and $\widetilde O(1/\varepsilon)$ for $α\ge2$, where $\widetilde O$ suppresses logarithmic factors. The first saves a factor $1/\varepsilon$; the second extends known integer-order scaling to noninteger orders. We also improve the von Neumann entropy bound from $\widetilde O(R/\varepsilon^2)$ to $\widetilde O(R/\varepsilon)$ and obtain $\widetilde O(R/\varepsilon)$ for noninteger Rényi orders $1<α<3$, with further bounds at other orders. Our functional-estimation theorem replaces the global product by a sum of local costs weighted by function magnitudes and spectral masses. We further estimate fixed logarithmic moments and entropy variance using $\widetilde O(R/\varepsilon)$ queries, providing efficient access to fluctuation parameters that enter finite-blocklength quantum compression and pure-state entanglement conversion. More broadly, our framework extends beyond entropy to a broad class of density-matrix functionals, offering a systematic approach toward optimal query complexity in quantum spectral estimation.

quant-pharXiv:2609.40179•Oct 1, 2026•0 citations

Sublinear quantum algorithms for estimating von Neumann entropy

Tom Gur, Min-Hsiu Hsieh, Sathyawageeswar Subramanian

Entropy is a fundamental property of both classical and quantum systems, spanning myriad theoretical and practical applications in physics and computer science. We study the problem of obtaining estimates to within a multiplicative factor $γ>1$ of the Shannon entropy of probability distributions and the von Neumann entropy of mixed quantum states. Our main results are: $\quad\bullet$ an $\widetilde{\mathcal{O}}\left( n^{\frac{1+η}{2γ^2}}\right)$-query quantum algorithm that outputs a $γ$-multiplicative approximation of the Shannon entropy $H(\mathbf{p})$ of a classical probability distribution $\mathbf{p} = (p_1,\ldots,p_n)$; $\quad\bullet$ an $\widetilde{\mathcal{O}}\left( n^{\frac12+\frac{1+η}{2γ^2}}\right)$-query quantum algorithm that outputs a $γ$-multiplicative approximation of the von Neumann entropy $S(ρ)$ of a density matrix $ρ\in\mathbb{C}^{n\times n}$. In both cases, the input is assumed to have entropy bounded away from zero by a quantity determined by the parameter $η>0$, since, as we prove, no polynomial query algorithm can multiplicatively approximate the entropy of distributions with arbitrarily low entropy. In addition, we provide $Ω\left(n^{\frac{1}{3γ^2}}\right)$ lower bounds on the query complexity of $γ$-multiplicative estimation of Shannon and von Neumann entropies. We work with the quantum purified query access model, which can handle both classical probability distributions and mixed quantum states, and is the most general input model considered in the literature.

arXiv:2111.11139•Nov 22, 2021•10 citations
We describe a quantum algorithm to estimate the \ensuremathα\ensuremath{\alpha}\ensuremathα-Renyi entropy of an unknown density matrix \ensuremathρ\ensuremath∈Cd\ifmmode×\else\texttimes\fid\ensuremath{\rho}\ensuremath{\in}{\mathbb{C}}^{d\ifmmode\times\else\texttimes\fi{}d}\ensuremathρ\ensuremath∈Cd\ifmmode×\else\texttimes\fid for \ensuremathα\ensuremath≠1\ensuremath{\alpha}\ensuremath{\ne}1\ensuremathα\ensuremath=1 by combining the recent technique of quantum singular value transformations with the method of estimating normalized traces in the one clean qubit model. We consider an oracular input model where the input state is prepared via a quantum oracle that outputs a purified version of the state, assumed to be nonsingular. Our method outputs an estimate of the \ensuremathα\ensuremath{\alpha}\ensuremathα-Renyi entropy to additive precision \ensuremathϵ\ensuremath{\epsilon}\ensuremathϵ, using an expected total number O(1(x\ensuremathϵ)2)O(\frac{1}{{(x\ensuremath{\epsilon})}^{2}})O((x\ensuremathϵ)21​) of independent applications of a quantum circuit which coherently queries the input unitary O(1\ensuremathδlogd\ensuremathϵ)O(\frac{1}{\ensuremath{\delta}}log\frac{d}{\ensuremath{\epsilon}})O(\ensuremathδ1​log\ensuremathϵd​) times, in each case measuring a single output qubit. Here \ensuremathδ\ensuremath{\delta}\ensuremathδ is a lower cutoff on the smallest eigenvalue of \ensuremathρ\ensuremath{\rho}\ensuremathρ and x=1dTr(\ensuremathρ\ensuremathα)x=\frac{1}{d}\mathrm{Tr}({\ensuremath{\rho}}^{\ensuremath{\alpha}})x=d1​Tr(\ensuremathρ\ensuremathα). The expected number of measurements made in this method can be compared to results in the sample complexity model that generally require \ensuremathΘ(d2\ensuremathϵ2)\mathrm{\ensuremath{\Theta}}(\frac{{d}^{2}}{{\ensuremath{\epsilon}}^{2}})\ensuremathΘ(\ensuremathϵ2d2​) samples. Furthermore, we also show that multiplicative approximations can be obtained by iteratively using additive approximations, with an overhead logarithmic in the dimension ddd.
We describe a quantum algorithm to estimate the α-Renyi entropy of an unknown density matrix ρ∈Cd×d for α≠1 by combining the recent technique of quantum singular value transformations with the method of estimating normalized traces in the one clean qubit model. We consider an oracular input model where the input state is prepared via a quantum oracle that outputs a purified version of the state, assumed to be nonsingular. Our method outputs an estimate of the α-Renyi entropy to additive precision ϵ, using an expected total number O(1(xϵ)2) of independent applications of a quantum circuit which coherently queries the input unitary O(1δlogdϵ) times, in each case measuring a single output qubit. Here δ is a lower cutoff on the smallest eigenvalue of ρ and x=1dTr(ρα). The expected number of measurements made in this method can be compared to results in the sample complexity model that generally require Θ(d2ϵ2) samples. Furthermore, we also show that multiplicative approximations can be obtained by iteratively using additive approximations, with an overhead logarithmic in the dimension d.
Estimating statistical properties is fundamental in statistics and computer science. In this paper, we propose a unified quantum algorithm framework for estimating properties of discrete probability distributions, with estimating Rényi entropies as specific examples. In particular, given a quantum oracle that prepares ann-dimensional quantum state Σni=1√pi|i⟩, for α > 1 and 0Hα(p) to within additive error ϵ with probability at least 2/3 using Õ(n1-1/2α/ϵ + √n/ϵ1+ 1/2α) and Õ(n1/2α/ϵ1+ 1/2α) queries, respectively. This improves the best known dependence in ϵ as well as the joint dependence betweennand 1/ϵ. Technically, our quantum algorithms combine quantum singular value transformation, quantum annealing, and variable-time amplitude estimation. We believe that our algorithm framework is of general interest and has wide applications.
Estimation of Shannon and Rényi entropies of unknown discrete distributions is a fundamental problem in statistical property testing. In this paper, we give the first quantum algorithms for estimating α-Rényi entropies (Shannon entropy being 1-Rényi entropy). In particular, we demonstrate a quadratic quantum speedup for Shannon entropy estimation and a generic quantum speedup for α-Rényi entropy estimation for all α ≥ 0 values, including tight bounds for the Shannon entropy, the Hartley entropy (α = 0), and the collision entropy (α = 2). We also provide quantum upper bounds for estimating min-entropy (α = +∞) as well as the Kullback-Leibler divergence. We complement our results with quantum lower bounds on α-Rényi entropy estimation for all α ≥ 0 values. Our approach is inspired by the pioneering work of Bravyi, Harrow, and Hassidim (BHH); however, with many new technical ingredients: 1) we improve the error dependence of the BHH framework by a fine-tuned error analysis together with Montanaro's approach to estimating the expected output of quantum subroutines for α = 0, 1; 2) we develop a procedure, similar to cooling schedules in simulated annealing, for general α ≥ 0, and 3) in the cases of integer α ≥ 2 and α = +∞, we reduce the entropy estimation problem to the α-distinctness and [log n]-distinctness problems, respectively.
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)H_α(\mathbf{p})Hα​(p) for any distribution p=(pi)i=1n\mathbf{p} = (p_i)_{i=1}^np=(pi​)i=1n​ given its purified quantum query-access. For non-integer α>1α> 1α>1, our algorithm estimates Hα(p)H_α(\mathbf{p})Hα​(p) to within additive error εεε using only O~(nmax⁡{1/2,1−1/α}/ε)\widetilde{\mathcal{O}}\big(n^{\max\{1/2,1-1/α\}}/ε\big)O(nmax{1/2,1−1/α}/ε) queries, which improves the best known upper bound for dependence on both nnn and 1/ε1/ε1/ε. For 0<α<10<α<10<α<1, our algorithm has query complexity O~(n1/(2α)/εmax⁡{1,1/(2α)})\widetilde{\mathcal{O}}\big(n^{1/(2α)}/ε^{\max\{1,1/(2α)\}}\big)O(n1/(2α)/εmax{1,1/(2α)}), which improves the dependence on 1/ε1/ε1/ε of the current best upper bound without incresing its nnn 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)})Ω\big(1/ε^{\max\{1,1/(2α)\}}\big)Ω(1/εmax{1,1/(2α)}) for 0<α<10<α<10<α<1, which identifies the optimal precision dependence of our algorithm.
The von Neumann, Tsallis, and Rényi entropies are fundamental measures of quantum information. A common estimation strategy multiplies the worst-case costs of spectral transformation and statistical readout, leaving gaps to query lower bounds. We reduce this overhead with multi-level algorithms that use local normalization and precision allocation, while variable-time estimation accounts for the probability of reaching expensive spectral tests. With controlled purified access and its inverse, we obtain bounds for additive error ε\varepsilonε, rank upper bound RRR, and fixed order ααα. Tsallis entropy estimation has near-optimal rank-independent query complexity O~(ε−1/(α−1))\widetilde O(\varepsilon^{-1/(α-1)})O(ε−1/(α−1)) for 1<α<21<α<21<α<2, when the allowed dimension and rank accommodate the lower-bound instances, and O~(1/ε)\widetilde O(1/\varepsilon)O(1/ε) for α≥2α\ge2α≥2, where O~\widetilde OO suppresses logarithmic factors. The first saves a factor 1/ε1/\varepsilon1/ε; the second extends known integer-order scaling to noninteger orders. We also improve the von Neumann entropy bound from O~(R/ε2)\widetilde O(R/\varepsilon^2)O(R/ε2) to O~(R/ε)\widetilde O(R/\varepsilon)O(R/ε) and obtain O~(R/ε)\widetilde O(R/\varepsilon)O(R/ε) for noninteger Rényi orders 1<α<31<α<31<α<3, with further bounds at other orders. Our functional-estimation theorem replaces the global product by a sum of local costs weighted by function magnitudes and spectral masses. We further estimate fixed logarithmic moments and entropy variance using O~(R/ε)\widetilde O(R/\varepsilon)O(R/ε) queries, providing efficient access to fluctuation parameters that enter finite-blocklength quantum compression and pure-state entanglement conversion. More broadly, our framework extends beyond entropy to a broad class of density-matrix functionals, offering a systematic approach toward optimal query complexity in quantum spectral estimation.
Entropy is a fundamental property of both classical and quantum systems, spanning myriad theoretical and practical applications in physics and computer science. We study the problem of obtaining estimates to within a multiplicative factor γ>1γ>1γ>1 of the Shannon entropy of probability distributions and the von Neumann entropy of mixed quantum states. Our main results are: ∙\quad\bullet∙ an O~(n1+η2γ2)\widetilde{\mathcal{O}}\left( n^{\frac{1+η}{2γ^2}}\right)O(n2γ21+η​)-query quantum algorithm that outputs a γγγ-multiplicative approximation of the Shannon entropy H(p)H(\mathbf{p})H(p) of a classical probability distribution p=(p1,…,pn)\mathbf{p} = (p_1,\ldots,p_n)p=(p1​,…,pn​); ∙\quad\bullet∙ an O~(n12+1+η2γ2)\widetilde{\mathcal{O}}\left( n^{\frac12+\frac{1+η}{2γ^2}}\right)O(n21​+2γ21+η​)-query quantum algorithm that outputs a γγγ-multiplicative approximation of the von Neumann entropy S(ρ)S(ρ)S(ρ) of a density matrix ρ∈Cn×nρ\in\mathbb{C}^{n\times n}ρ∈Cn×n. In both cases, the input is assumed to have entropy bounded away from zero by a quantity determined by the parameter η>0η>0η>0, since, as we prove, no polynomial query algorithm can multiplicatively approximate the entropy of distributions with arbitrarily low entropy. In addition, we provide Ω(n13γ2)Ω\left(n^{\frac{1}{3γ^2}}\right)Ω(n3γ21​) lower bounds on the query complexity of γγγ-multiplicative estimation of Shannon and von Neumann entropies. We work with the quantum purified query access model, which can handle both classical probability distributions and mixed quantum states, and is the most general input model considered in the literature.