Multiparameter function estimation for general Hamiltonians
- URL: http://arxiv.org/abs/2605.04136v1
- Date: Tue, 05 May 2026 18:00:00 GMT
- Title: Multiparameter function estimation for general Hamiltonians
- Abstract summary: We derive the ultimate quantum limit and present an estimation protocol for any function of parameters in a general Hamiltonian.<n>Our result unifies and extends previous works, providing a general framework for optimal function estimation in quantum systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Estimation of physical parameters encoded in a Hamiltonian is a central task in quantum sensing and learning. While the ultimate precision limit for estimating a single parameter coupled to a single generator is well established, the corresponding bound for estimating a function of multiple parameters-each coupled to distinct and possibly non-commuting generators-remains unknown in general. Here, we derive the ultimate quantum limit and present an estimation protocol for any function of parameters in a general Hamiltonian that attains this bound. We show that, although the task is fundamentally a multiparameter problem, our tight bound reduces to an optimized single-parameter quantum Cramér-Rao bound, even for arbitrary generator sets. Our result unifies and extends previous works, providing a general framework for optimal function estimation in quantum systems.
Related papers
- Error-corrected function estimation advantage in multiparameter Hamiltonians [3.0446222918423627]
We establish the ultimate precision limits for estimating a function of multiple Hamiltonian parameters in the presence of Markovian noise.<n>We show that direct function estimation can substantially outperform estimating all parameters individually and subsequently evaluating the function.
arXiv Detail & Related papers (2026-09-14T18:57:24Z) - Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions [31.082191748525137]
We give a PACBayesian account in which generalization is governed by the effective dimension of the Fisher geometry induced by the circuit.<n>We find that circuits with larger Fisher effective dimension exhibit larger train-test gaps, while parameter count is a weak predictor.<n>We validate this mechanism across supervised classification, quantum contextual bandits, and value-function generalizations.
arXiv Detail & Related papers (2026-07-07T12:57:46Z) - Approaching the Limit in Multiparameter AC Magnetometry with Quantum Control [22.22957615478987]
We introduce a quantum control protocol that resolves the problem of estimating the amplitude and frequency of an AC magnetic field.<n>It not only removes the singularity but also restores the optimal scaling of precision with interrogation time for both parameters simultaneously.<n>We experimentally validate this protocol using a nitrogen-vacancy center in diamond at room temperature, demonstrating the concurrent achievement of the optimal scaling for both parameters.
arXiv Detail & Related papers (2026-02-19T18:55:17Z) - Performance Guarantees for Quantum Neural Estimation of Entropies [31.955071410400947]
Quantum neural estimators (QNEs) combine classical neural networks with parametrized quantum circuits.<n>We study formal guarantees for QNEs of measured relative entropies in the form of non-asymptotic error risk bounds.<n>Our theory aims to facilitate principled implementation of QNEs for measured relative entropies.
arXiv Detail & Related papers (2025-11-24T16:36:06Z) - The Most Informative Cramér--Rao Bound for Quantum Two-Parameter Estimation with Pure State Probes [0.0]
We present a new expression for the achievable bound for two- parameter estimation with pure states.<n>We also determine the optimal measurements.<n>To demonstrate the utility of our result, we determine the precision limit for estimating displacements using grid states.
arXiv Detail & Related papers (2025-11-18T22:15:14Z) - Quantum Boltzmann machine learning of ground-state energies [3.187381965457262]
Esting the ground-state energy of Hamiltonians is a fundamental task for which quantum computers can be helpful.
We analyze the performance of quantum Boltzmann machines for this task.
Our algorithm estimates the gradient of the energy function efficiently by means of a novel quantum circuit construction.
arXiv Detail & Related papers (2024-10-16T18:22:03Z) - 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) - Estimating many properties of a quantum state via quantum reservoir
processing [2.5432391525687748]
We propose a general framework for constructing classical approximations of arbitrary quantum states with quantum reservoirs.
A key advantage of our method is that only a single local measurement setting is required for estimating arbitrary properties.
This estimation scheme is extendable to higher-dimensional systems and hybrid systems with non-identical local dimensions.
arXiv Detail & Related papers (2023-05-11T15:21:21Z) - Generalized quantum circuit differentiation rules [23.87373187143897]
Variational quantum algorithms that are used for quantum machine learning rely on the ability to automatically differentiate parametrized quantum circuits.
Here, we propose the rules for differentiating quantum circuits (unitaries) with arbitrary generators.
arXiv Detail & Related papers (2021-08-03T00:29:45Z) - 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) - Measuring Analytic Gradients of General Quantum Evolution with the
Stochastic Parameter Shift Rule [0.0]
We study the problem of estimating the gradient of the function to be optimized directly from quantum measurements.
We derive a mathematically exact formula that provides an algorithm for estimating the gradient of any multi-qubit parametric quantum evolution.
Our algorithm continues to work, although with some approximations, even when all the available quantum gates are noisy.
arXiv Detail & Related papers (2020-05-20T18:24:11Z) - One from many: Estimating a function of many parameters [0.0]
Many parameters of a process are unknown; estimate a specific linear combination of these parameters without having the ability to control any of the parameters.
Geometric reasoning demonstrates the conditions, necessary and sufficient, for saturating the fundamental and attainable quantum-process bound.
arXiv Detail & Related papers (2020-02-07T16:55:25Z)
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.