Uniqueness and Cramér-Rao Efficiency of Quantum U-Statistics
- URL: http://arxiv.org/abs/2609.08745v1
- Date: Tue, 08 Sep 2026 13:37:19 GMT
- Title: Uniqueness and Cramér-Rao Efficiency of Quantum U-Statistics
- Abstract summary: We study unbiased estimation of scalar-valued functionals of quantum states from independent copies.<n>We prove that, among unbiased permutation-invariant estimators, the quantum U-statistic is the unique extension to an arbitrary number of copies.
- Score: 8.156494881838947
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study unbiased estimation of scalar-valued polynomial functionals of quantum states from independent copies. We establish an equivalence between the first-order marginal of a permutation-invariant finite-copy observable and the functional gradient. We then prove that, among unbiased permutation-invariant estimators, the quantum U-statistic is the unique extension to an arbitrary number of copies. We further derive a universal variance expansion in which the leading $1/n$ term is determined by the variance of the functional gradient, while higher-order contributions are of order $O(1/n^2)$. This leading variance coincides with the multiparameter quantum Cramér-Rao limit, establishing asymptotic efficiency of quantum U-statistics. We also characterize the higher-order scaling at points where the variance of the first-order gradient vanishes. As an application, we analyze the Bures $χ^2$-divergence and show that a spectral lower bound on the reference state is sufficient but not necessary for bounded-variance estimation.
Related papers
- Hierarchical Fourier Approximation for Variational Quantum Distribution Learning [0.0]
We study quantum distribution learning through a hierarchy of Walsh-Fourier approximations on the Boolean cube.<n>We prove an end-to-end expected learning guarantee where the approximation term is determined by the omitted Fourier mass.<n>We identify the conditions under which low-to-high spectral training admits approximation-estimation-optimization analysis.
arXiv Detail & Related papers (2026-09-05T23:34:49Z) - Spectrum Estimation is Almost as Hard as Tomography [1.7205106391379026]
We prove a sample complexity lower bound of $(d2-)$ for spectrum estimation to constant sorted total-variation error.<n>Our hard instances are constructed from sandwiched products of Haar-random projectors.
arXiv Detail & Related papers (2026-07-31T17:57:03Z) - Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise [49.730496294398726]
We develop a novel quantum mean estimator for heavy-tailed random variables.<n>We derive stronger lower bounds for tail index>4/3$, showing that a nontrivial dependence on a dimension is unavoidable.<n>For a convex objective function, we propose a quantum projected gradient descent method.
arXiv Detail & Related papers (2026-07-28T09:29:37Z) - Regularized Variational and Spectral Log-Density-Ratio Estimation in the Gaussian Location Model [0.554780083433538]
We study ridge-regularized log-density-ratio estimation in the Gaussian location model.<n>The variational estimator is the empirical Kullback-Leibler (KL) log-normalized fit with a squared L2-penalty on its nonconstant coefficient.
arXiv Detail & Related papers (2026-07-02T08:46:52Z) - Cumulant-based quantum relative Rényi functional [0.0]
We introduce a new cumulant-based quantum relative Rényi functional as a candidate quantum Rényi divergence.<n>The functional naturally admits a path-integral-like representation through the Lie-Trotter product expansion.<n>We show that the resulting relative quantumness quantity vanishes if and only if the underlying states commute.
arXiv Detail & Related papers (2026-06-30T06:34:53Z) - Quantum $f$-divergences and Their Local Behaviour: An Analysis via Relative Expansion Coefficients [4.30484058393522]
We study contraction and expansion coefficients, which can be combined into a single emphrelative expansion coefficient<n>We identify new families of $f$ for which the global ($f$ divergence) and local (Riemannian) relative expansion coefficients coincide for every pair of channels.<n>We prove a reverse quantum Markov convergence theorem, converting positive expansion coefficients into quantitative lower bounds on the convergence rate.
arXiv Detail & Related papers (2025-10-07T17:44:37Z) - Error exponents of quantum state discrimination with composite correlated hypotheses [40.82628972269358]
We study the error exponents in quantum hypothesis testing between two sets of quantum states.<n>We introduce and compare two natural extensions of the quantum Hoeffding divergence and anti-divergence to sets of quantum states.
arXiv Detail & Related papers (2025-08-18T13:04:06Z) - Reducing the sampling complexity of energy estimation in quantum many-body systems using empirical variance information [45.18582668677648]
We consider the problem of estimating the energy of a quantum state preparation for a given Hamiltonian in Pauli decomposition.<n>We construct an adaptive estimator using the state's actual variance.
arXiv Detail & Related papers (2025-02-03T19:00:01Z) - Sample-Efficient Quantum State Tomography for Structured Quantum States in One Dimension [25.333797381352973]
Many physical quantum states often exhibit certain low-dimensional structures that enable the development of efficient QST.<n>We show that the number of state copies to guarantee bounded recovery error of an MPO state with a constrained least-squares estimator depends on the probability distribution of the MPO.<n>We prove that only a number of state copies proportional to the number of independent parameters in the MPO is sufficient for a guaranteed recovery of any state represented by an MPO.
arXiv Detail & Related papers (2024-10-03T15:26:26Z) - Quantum speedups for stochastic optimization [18.32349609443295]
We consider the problem of minimizing a continuous function given quantumvitzvariance to an oracle.
We provide two new methods for the special case of minimizing a Lipsch avvitz function.
arXiv Detail & Related papers (2023-08-03T07:39:10Z) - Policy evaluation from a single path: Multi-step methods, mixing and
mis-specification [45.88067550131531]
We study non-parametric estimation of the value function of an infinite-horizon $gamma$-discounted Markov reward process.
We provide non-asymptotic guarantees for a general family of kernel-based multi-step temporal difference estimates.
arXiv Detail & Related papers (2022-11-07T23:15:25Z) - Optimal policy evaluation using kernel-based temporal difference methods [78.83926562536791]
We use kernel Hilbert spaces for estimating the value function of an infinite-horizon discounted Markov reward process.
We derive a non-asymptotic upper bound on the error with explicit dependence on the eigenvalues of the associated kernel operator.
We prove minimax lower bounds over sub-classes of MRPs.
arXiv Detail & Related papers (2021-09-24T14:48:20Z) - On the properties of the asymptotic incompatibility measure in
multiparameter quantum estimation [62.997667081978825]
Incompatibility (AI) is a measure which quantifies the difference between the Holevo and the SLD scalar bounds.
We show that the maximum amount of AI is attainable only for quantum statistical models characterized by a purity larger than $mu_sf min = 1/(d-1)$.
arXiv Detail & Related papers (2021-07-28T15:16:37Z) - On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and
Non-Asymptotic Concentration [115.1954841020189]
We study the inequality and non-asymptotic properties of approximation procedures with Polyak-Ruppert averaging.
We prove a central limit theorem (CLT) for the averaged iterates with fixed step size and number of iterations going to infinity.
arXiv Detail & Related papers (2020-04-09T17:54:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.