Optimal frequency estimation and its application to quantum dots
- URL: http://arxiv.org/abs/2004.12049v2
- Date: Tue, 16 Feb 2021 14:00:26 GMT
- Title: Optimal frequency estimation and its application to quantum dots
- Authors: Angel Gutierrez-Rubio, Peter Stano, Daniel Loss
- Abstract summary: We address the interaction-time optimization for frequency estimation in a two-level system.
We devise novel estimation protocols with and without feedback.
It can improve current experimental techniques and boost coherence times in quantum computing.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We address the interaction-time optimization for frequency estimation in a
two-level system. The goal is to estimate with maximum precision a stochastic
perturbation. Our approach is valid for any figure of merit used to define
optimality, and is illustrated for the variance and entropy. For the entropy,
we clarify the connection to maximum-likelihood estimation. We devise novel
estimation protocols with and without feedback. They outperform common
protocols given in the literature. We design a probabilistic self-consistent
protocol as an optimal estimation without feedback. It can improve current
experimental techniques and boost coherence times in quantum computing.
Related papers
- Interplay between time and energy in bosonic noisy quantum metrology [0.0]
We show that there is a nontrivial interplay between the average energy and the time devoted to the estimation.
We show how the time employed in the estimation should be partitioned in order to achieve the best possible precision.
arXiv Detail & Related papers (2024-09-27T14:41:09Z) - Optimising the relative entropy under semi definite constraints -- A new tool for estimating key rates in QKD [0.0]
Finding the minimal relative entropy of two quantum states under semi definite constraints is a pivotal problem.
We provide a method that addresses this optimisation.
We build on a recently introduced integral representation of quantum relative entropy by P.E. Frenkel.
arXiv Detail & Related papers (2024-04-25T20:19:47Z) - 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) - 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) - Importance sampling for stochastic quantum simulations [68.8204255655161]
We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients.
We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage.
Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
arXiv Detail & Related papers (2022-12-12T15:06:32Z) - Real-time adaptive estimation of decoherence timescales for a single
qubit [2.6938732235832044]
Characterising the time over which quantum coherence survives is critical for any implementation of quantum bits, memories and sensors.
We present an adaptive multi- parameter approach, based on a simple analytical update rule, to estimate the key decoherence in real time.
A further speed-up of a factor $sim 2$ can be realised by performing our optimisation with respect to sensitivity as opposed to variance.
arXiv Detail & Related papers (2022-10-12T11:28:23Z) - Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time
Guarantees [56.848265937921354]
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy.
Many algorithms for IRL have an inherently nested structure.
We develop a novel single-loop algorithm for IRL that does not compromise reward estimation accuracy.
arXiv Detail & Related papers (2022-10-04T17:13:45Z) - Robust and Adaptive Temporal-Difference Learning Using An Ensemble of
Gaussian Processes [70.80716221080118]
The paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning.
The OS-GPTD approach is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs.
To alleviate the limited expressiveness associated with a single fixed kernel, a weighted ensemble (E) of GP priors is employed to yield an alternative scheme.
arXiv Detail & Related papers (2021-12-01T23:15:09Z) - Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation [87.54604263202941]
We propose a tiny deep neural network of which partial layers are iteratively exploited for refining its previous estimations.
We employ learned gating criteria to decide whether to exit from the weight-sharing loop, allowing per-sample adaptation in our model.
Our method consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches in terms of both accuracy and efficiency for widely used benchmarks.
arXiv Detail & Related papers (2021-11-11T23:31:34Z) - Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box
Optimization Framework [100.36569795440889]
This work is on the iteration of zero-th-order (ZO) optimization which does not require first-order information.
We show that with a graceful design in coordinate importance sampling, the proposed ZO optimization method is efficient both in terms of complexity as well as as function query cost.
arXiv Detail & Related papers (2020-12-21T17:29:58Z)
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.