QuanEstimation: An open-source toolkit for quantum parameter estimation

• URL: http://arxiv.org/abs/2205.15588v3
• Date: Tue, 25 Oct 2022 03:19:31 GMT
• Title: QuanEstimation: An open-source toolkit for quantum parameter estimation
• Authors: Mao Zhang, Huai-Ming Yu, Haidong Yuan, Xiaoguang Wang, Rafa{\l} Demkowicz-Dobrza\'nski, Jing Liu
• Abstract summary: We present a Python-Julia-based open-source toolkit for quantum parameter estimation. It includes many well-used mathematical bounds and optimization methods.
• Score: 4.648493096183626
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quantum parameter estimation promises a high-precision measurement in theory, however, how to design the optimal scheme in a specific scenario, especially under a practical condition, is still a serious problem that needs to be solved case by case due to the existence of multiple mathematical bounds and optimization methods. Depending on the scenario considered, different bounds may be more or less suitable, both in terms of computational complexity and the tightness of the bound itself. At the same time, the metrological schemes provided by different optimization methods need to be tested against realization complexity, robustness, etc. Hence, a comprehensive toolkit containing various bounds and optimization methods is essential for the scheme design in quantum metrology. To fill this vacancy, here we present a Python-Julia-based open-source toolkit for quantum parameter estimation, which includes many well-used mathematical bounds and optimization methods. Utilizing this toolkit, all procedures in the scheme design, such as the optimizations of the probe state, control and measurement, can be readily and efficiently performed.

Related papers

• QMetro++ -- Python optimization package for large scale quantum metrology with customized strategy structures [0.0]
  QMetro++ is a Python package dedicated to identifying optimal estimation protocols.<n>The package comes with an implementation of the recently developed methods for computing fundamental upper bounds on QFI.
  arXiv  Detail & Related papers  (2025-06-19T18:13:22Z)
• Convergence and efficiency proof of quantum imaginary time evolution for bounded order systems [0.0]
  We show that the imaginary time evolution guarantees convergence to the global minimum without critical slowing down.<n>We also show that the compilation process can be performed efficiently up to an arbitrary error threshold if the underlying physical system is of bounded order.
  arXiv  Detail & Related papers  (2025-06-03T15:52:06Z)
• A Novel Unified Parametric Assumption for Nonconvex Optimization [53.943470475510196]
  Non optimization is central to machine learning, but the general framework non convexity enables weak convergence guarantees too pessimistic compared to the other hand. We introduce a novel unified assumption in non convex algorithms.
  arXiv  Detail & Related papers  (2025-02-17T21:25:31Z)
• Implementing transferable annealing protocols for combinatorial optimisation on neutral atom quantum processors: a case study on smart-charging of electric vehicles [1.53934570513443]
  In this paper, we build on the promising potential of parameter transferability across problem instances with similar local structures. Our study reveals that, for Maximum Independent Set problems on graph families with shared geometries, optimal parameters naturally concentrate. We apply this method to address a smart-charging optimisation problem on a real dataset.
  arXiv  Detail & Related papers  (2024-11-25T18:41:02Z)
• QestOptPOVM: An iterative algorithm to find optimal measurements for quantum parameter estimation [17.305295658536828]
  We introduce an algorithm, termed QestPOVM, designed to directly identify optimal positive operator-Opt measure (POVM) Through rigorous testing on several examples for multiple copies of qubit states (up to six copies), we demonstrate the efficiency and accuracy of our proposed algorithm. Our algorithm functions as a tool for elucidating the explicit forms of optimal POVMs, thereby enhancing our understanding of quantum parameter estimation methodologies.
  arXiv  Detail & Related papers  (2024-03-29T11:46:09Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• Finding the optimal probe state for multiparameter quantum metrology using conic programming [61.98670278625053]
  We present a conic programming framework that allows us to determine the optimal probe state for the corresponding precision bounds. We also apply our theory to analyze the canonical field sensing problem using entangled quantum probe states.
  arXiv  Detail & Related papers  (2024-01-11T12:47:29Z)
• Designing optimal protocols in Bayesian quantum parameter estimation with higher-order operations [0.0]
  A major task in quantum sensing is to design the optimal protocol, i.e., the most precise one. Here, we focus on the single-shot Bayesian setting, where the goal is to find the optimal initial state of the probe. We leverage the formalism of higher-order operations to develop a method that finds a protocol that is close to the optimal one with arbitrary precision.
  arXiv  Detail & Related papers  (2023-11-02T18:00:36Z)
• End-to-end resource analysis for quantum interior point methods and portfolio optimization [63.4863637315163]
  We provide a complete quantum circuit-level description of the algorithm from problem input to problem output. We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm.
  arXiv  Detail & Related papers  (2022-11-22T18:54:48Z)
• 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)
• Experimental multiparameter quantum metrology in adaptive regime [0.0]
  We demonstrate the simultaneous estimation of three optical phases in a programmable integrated photonic circuit. Results show the possibility of successfully combining different fundamental methodologies towards transition to quantum sensors applications.
  arXiv  Detail & Related papers  (2022-08-30T18:02:51Z)
• Certificates of quantum many-body properties assisted by machine learning [0.0]
  We propose a novel approach combining the power of relaxation techniques with deep reinforcement learning. We illustrate the viability of the method in the context of finding the ground state energy of many transfer systems. We provide tools to generalize the approach to other common applications in the field of quantum information processing.
  arXiv  Detail & Related papers  (2021-03-05T17:47:26Z)
• 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)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)

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.