Quantum Barankin bounds beyond local unbiasedness: Analytic results for Gaussian states via a right-division framework
- URL: http://arxiv.org/abs/2609.10157v1
- Date: Wed, 09 Sep 2026 13:32:47 GMT
- Title: Quantum Barankin bounds beyond local unbiasedness: Analytic results for Gaussian states via a right-division framework
- Abstract summary: We propose a quantum version of the Barankin bound as an alternative to the quantum Cramér-Rao bound for quantum parameter estimation.<n>In particular, unbiasedness over the entire parameter space can be imposed, yielding precision limits for globally unbiased quantum parameter estimation.
- Score: 0.1631115063641726
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a quantum version of the Barankin bound as an alternative to the quantum Cramér-Rao bound for quantum parameter estimation. The quantum Barankin bound provides a lower bound on the mean squared error of estimators satisfying arbitrarily chosen bias constraints at arbitrarily chosen parameter points. In particular, unbiasedness over the entire parameter space can be imposed, yielding precision limits for globally unbiased quantum parameter estimation. In contrast to the recently derived quantum Barankin bound, which is based on a symmetric division superoperator, our bound is based on a nonsymmetric right-division superoperator. This formulation enables us to find analytic expressions for the Barankin matrix for Gaussian states, which allows for an efficient calculation of the bound. We demonstrate the usefulness of our results by applying them to various examples that exhibit the threshold effect in the few-shot regime, which is invisible to the standard quantum Cramér-Rao bound approach.
Related papers
- Quantum Decision Theory for Displacement Detection with Finite-Energy GKP States [46.2474896050764]
We develop a framework for detecting phase-space displacements with finite-energy, $d$-level Gottesman-Kitaev-Preskill (GKP) probes.<n>For single-mode and entanglement-assisted architectures, we derive the Bayesian minimum-error probability.<n>We benchmark the resulting protocols against coherent-state, direction-matched squeezed-vacuum, and twin-beam schemes.
arXiv Detail & Related papers (2026-08-08T10:32:05Z) - Bayesian Monotone Metrics for Multiparameter Quantum Estimation [4.669017653337737]
We introduce Bayesian monotone metrics by evaluating Petz monotone metrics on the prior-averaged state.<n>For every monotone metric in the family, we prove a universal dominance over the corresponding quantum van Trees.<n>Our results establish Bayesian monotone metrics as a unifying information-geometric perspective on Bayesian quantum estimation.
arXiv Detail & Related papers (2026-07-02T04:27:28Z) - Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization [50.880384999888044]
We develop misspecified GP bandit algorithms and derive regret bounds that characterize the trade-off between approximation error and information gain.<n>Our methods outperform full quantum kernels in sample efficiency, while substantially reducing computational overhead.
arXiv Detail & Related papers (2026-07-01T15:38:00Z) - Grassmann Variational Monte Carlo with neural wave functions [45.935798913942904]
We formalize the framework introduced by Pfau et al.citepfau2024accurate in terms of Grassmann geometry of the Hilbert space.<n>We validate our approach on the Heisenberg quantum spin model on the square lattice, achieving highly accurate energies and physical observables for a large number of excited states.
arXiv Detail & Related papers (2025-07-14T13:53:13Z) - Generalization Bounds for Quantum Learning via Rényi Divergences [45.45698347373077]
This work advances the theoretical understanding of quantum learning by establishing a new family of upper bounds on the expected generalization error of quantum learning algorithms.<n>Our primary contribution is the derivation of these bounds in terms of quantum and classical R'enyi divergences.
arXiv Detail & Related papers (2025-05-16T09:21:31Z) - Determining the ability for universal quantum computing: Testing
controllability via dimensional expressivity [39.58317527488534]
Controllability tests can be used in the design of quantum devices to reduce the number of external controls.
We devise a hybrid quantum-classical algorithm based on a parametrized quantum circuit.
arXiv Detail & Related papers (2023-08-01T15:33:41Z) - Quantum Conformal Prediction for Reliable Uncertainty Quantification in
Quantum Machine Learning [47.991114317813555]
Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots.
This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
arXiv Detail & Related papers (2023-04-06T22:05:21Z) - Hierarchies of Frequentist Bounds for Quantum Metrology: From
Cram\'er-Rao to Barankin [0.0]
We obtain hierarchies of increasingly tight bounds that include the quantum Cram'er-Rao bound at the lowest order.
Results reveal generalizations of the quantum Fisher information that are able to avoid regularity conditions.
arXiv Detail & Related papers (2023-03-10T17:55:52Z) - Analyzing Prospects for Quantum Advantage in Topological Data Analysis [35.423446067065576]
We analyze and optimize an improved quantum algorithm for topological data analysis.
We show that super-quadratic quantum speedups are only possible when targeting a multiplicative error approximation.
We argue that quantum circuits with tens of billions of Toffoli can solve seemingly classically intractable instances.
arXiv Detail & Related papers (2022-09-27T17:56:15Z) - Quantum Cram\'er-Rao bound for quantum statistical models with
parameter-dependent rank [0.0]
We argue that the limiting version of quantum Cram'er-Rao bound still holds when the parametric density operator changes its rank.
We analyze a typical example of the quantum statistical models with parameter-dependent rank.
arXiv Detail & Related papers (2021-11-01T13:23:04Z) - Incompatibility measures in multi-parameter quantum estimation under
hierarchical quantum measurements [4.980960723762946]
We show an approach to study the incompatibility under general $p$-local measurements.
We demonstrate the power of the approach by presenting a hierarchy of analytical bounds on the tradeoff.
arXiv Detail & Related papers (2021-09-13T09:33:47Z) - 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) - Quasi-probability distributions in Loop Quantum Cosmology [0.0]
We introduce a complete family of parametrized quasi-probability distributions in phase space and their corresponding Weyl quantization maps.
We expect our results may serve to analyze several fundamental aspects within the Loop Quantum Cosmology program.
arXiv Detail & Related papers (2020-07-02T18:05:32Z)
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.