Optimal scheme for distributed quantum metrology
- URL: http://arxiv.org/abs/2509.18334v2
- Date: Thu, 25 Sep 2025 19:37:08 GMT
- Title: Optimal scheme for distributed quantum metrology
- Authors: Zhiyao Hu, Allen Zang, Jianwei Wang, Tian Zhong, Haidong Yuan, Liang Jiang, Zain H. Saleem,
- Abstract summary: We develop optimal schemes for distributed quantum metrology that characterize the ultimate precision limits in distributed systems.<n>We prove that the optimal control operations can be implemented locally on each sensor, eliminating the need for non-local control operations across distant nodes.
- Score: 3.330069889084255
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Optimal strategies for local quantum metrology -- including the preparation of optimal probe states, implementation of optimal control and measurement strategies, are well established. However, for distributed quantum metrology, where the goal is to estimate global properties of multiple spatially distributed parameters, the optimal scheme -- particularly the role of optimal control -- remains poorly understood. In this work, we address this challenge by developing optimal schemes for distributed quantum metrology that characterize the ultimate precision limits in distributed systems. We derive the optimal probe state, optimal control protocols, and measurement strategies in estimating a linear combination of $N$ independent unknown parameters coupled to $d$ networked sensors. Crucially, we prove that the optimal control operations can be implemented locally on each sensor, eliminating the need for non-local control operations across distant nodes. This result significantly reduces the complexity of implementing optimal strategies in distributed quantum metrology. To demonstrate the power of our framework, we apply it to several key scenarios.
Related papers
- Optimal Control Design Guided by Adam Algorithm and LSTM-Predicted Open Quantum System Dynamics [0.0]
Long short-term memory neural networks (LSTM-NNs) can accurately predict the time evolution of open quantum systems.<n>We propose an optimal control framework for rapid and efficient optimal control design in open quantum systems.
arXiv Detail & Related papers (2026-02-04T12:08:12Z) - Optimal qudit overlapping tomography and optimal measurement order [14.6984428694541]
Overlapping tomography is essential for characterizing quantum systems, but it becomes infeasible for large systems due to exponential resource scaling.<n>Here, we investigate optimal qudit overlapping tomography, constructing local measurement settings from generalized Gell-Mann matrices.<n>We prove that pairwise tomography requires at most $8 + 56leftlceil log_8 n rightrceil$ measurement settings, and provide an explicit scheme achieving this bound.
arXiv Detail & Related papers (2026-01-15T04:24:07Z) - Optimal Control of Coupled Sensor-Ancilla Qubits for Multiparameter Estimation [0.0]
We numerically investigate optimal control of a two-qubit sensor-ancilla system coupled via an Ising term.<n>We achieve robust convergence and high precision across a range of interaction strengths and field configurations.
arXiv Detail & Related papers (2025-12-12T15:53:07Z) - Efficient adaptive control strategy for multi-parameter quantum metrology in two-dimensional systems [0.0]
We propose an efficient adaptive control strategy for multi- parameter quantum metrology in two-dimensional systems.<n>By eliminating the trade-offs among optimal measurements, initial states, and control Hamiltonians, we derive an explicit relation between the estimator variance and evolution time.<n>The proposed strategy achieves the optimal performance up to an overall factor of constant order with only a few iterations.
arXiv Detail & Related papers (2025-10-16T15:47:55Z) - Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
arXiv Detail & Related papers (2025-09-12T18:41:10Z) - Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices [0.0347577906896546]
We present the results of a comprehensive study of optimization algorithms for the calibration of quantum devices.<n>Our benchmark includes widely used algorithms such as Nelder-Mead and the state-of-the-art Covariance Matrix Adaptation Evolution Strategy (CMA-ES)<n>Based on our findings, we recommend the CMA-ES algorithm and provide empirical evidence for its superior performance across all tested scenarios.
arXiv Detail & Related papers (2025-09-10T13:00:40Z) - Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference [49.11857020431547]
We show that a trust region based strategy can be understood as a geometric annealing from the prior to the target measure.<n>We demonstrate in multiple optimal control applications that our novel method can improve performance significantly.
arXiv Detail & Related papers (2025-08-17T22:10:35Z) - Application of the Pontryagin Maximum Principle to the robust time-optimal control of two-level quantum systems [3.5621685463862356]
We study the time-optimal robust control of a two-level quantum system subjected to field inhomogeneities.<n>We apply the Pontryagin Maximum Principle and we introduce a reduced space onto which the optimal dynamics is projected down.
arXiv Detail & Related papers (2025-03-14T19:47:08Z) - Substantial precision enhancements via adaptive symmetry-informed Bayesian metrology [2.477017847456471]
In-depth optimisation of measurement procedures beyond phase estimation has been overlooked.<n>We present a systematic strategy for parameter estimation that can be applied across a wide range of experimental platforms.<n>We demonstrate the power of this strategy by applying it to atom number estimation in a quantum technology experiment.
arXiv Detail & Related papers (2024-10-14T15:20:13Z) - Fully-Optimized Quantum Metrology: Framework, Tools, and Applications [8.98216737402976]
The tutorial consists of a pedagogic introduction to the background and mathematical tools of optimal quantum metrology.
The approach can identify the optimal precision for different sets of strategies, including parallel, sequential, quantum SWITCH-enhanced, causally superposed, and generic indefinite-causal-order strategies.
arXiv Detail & Related papers (2024-09-11T07:36:40Z) - Stochastic Optimal Control Matching [53.156277491861985]
Our work introduces Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for optimal control.
The control is learned via a least squares problem by trying to fit a matching vector field.
Experimentally, our algorithm achieves lower error than all the existing IDO techniques for optimal control.
arXiv Detail & Related papers (2023-12-04T16:49:43Z) - Maximize to Explore: One Objective Function Fusing Estimation, Planning,
and Exploration [87.53543137162488]
We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX.
textttMEX integrates estimation and planning components while balancing exploration exploitation automatically.
It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
arXiv Detail & Related papers (2023-05-29T17:25:26Z) - Optimal State Manipulation for a Two-Qubit System Driven by Coherent and
Incoherent Controls [77.34726150561087]
State preparation is important for optimal control of two-qubit quantum systems.
We exploit two physically different coherent control and optimize the Hilbert-Schmidt target density matrices.
arXiv Detail & Related papers (2023-04-03T10:22:35Z) - Tight Cram\'{e}r-Rao type bounds for multiparameter quantum metrology
through conic programming [61.98670278625053]
It is paramount to have practical measurement strategies that can estimate incompatible parameters with best precisions possible.
Here, we give a concrete way to find uncorrelated measurement strategies with optimal precisions.
We show numerically that there is a strict gap between the previous efficiently computable bounds and the ultimate precision bound.
arXiv Detail & Related papers (2022-09-12T13:06:48Z) - Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.
We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT)
Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
arXiv Detail & Related papers (2020-10-01T18:14:11Z) - Experimental adaptive Bayesian estimation of multiple phases with
limited data [0.0]
adaptive protocols, exploiting additional control parameters, provide a tool to optimize the performance of a quantum sensor to work in such limited data regime.
Finding the optimal strategies to tune the control parameters during the estimation process is a non-trivial problem, and machine learning techniques are a natural solution to address such task.
We employ a compact and flexible integrated photonic circuit, fabricated by femtosecond laser writing, which allows to implement different strategies with high degree of control.
arXiv Detail & Related papers (2020-02-04T11:32:32Z)
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.