Efficient real-time spin readout of nitrogen-vacancy centers based on Bayesian estimation

• URL: http://arxiv.org/abs/2302.06310v1
• Date: Mon, 13 Feb 2023 12:19:13 GMT
• Title: Efficient real-time spin readout of nitrogen-vacancy centers based on Bayesian estimation
• Authors: Jixing Zhang, Tianzheng Liu, Sigang Xia, Guodong Bian, Pengcheng Fan, Mingxin Li, Sixian Wang, Xiangyun Li, Chen Zhang, Shaoda Zhang, and Heng Yuan
• Abstract summary: A real-time Bayesian estimation algorithm is proposed to improve the spin readout efficiency of the nitrogen vacancy (NV) center. It is anticipated that the employed Bayesian estimation readout will effectively present superior sensing capabilities of the NV ensemble.
• Score: 2.759631614157892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, to improve the spin readout efficiency of the nitrogen vacancy (NV) center, a real-time Bayesian estimation algorithm is proposed, which combines both the prior probability distribution and the fluorescence likelihood function established by the implementation of the NV center dynamics model. The theoretical surpass of the Cramer-Rao lower bound of the readout variance and the improvement of the readout efficiency in the simulation indicate that our approach is an appealing alternative to the conventional photon summation method. The Bayesian real-time estimation readout was experimentally realized by combining a high-performance acquisition and processing hardware, and the Rabi oscillation experiments divulged that the signal-to-noise ratio of our approach was improved by 28.6%. Therefore, it is anticipated that the employed Bayesian estimation readout will effectively present superior sensing capabilities of the NV ensemble, and foster the further development of compact and scalable quantum sensors and consequently novel quantum information processing devices on a monolithic platform.

Related papers

• Mitigating Barren plateaus in quantum denoising diffusion probabilistic models [49.90716699848553]
  Quantum generative models leverage quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data.<n>QuDDPM has been proposed as a promising framework for quantum generative learning.<n>We show that barren plateaus emerge in QuDDPMs due to the use of 2-design states as the input for the denoising process.<n>We introduce an improved QuDDPM that utilizes a distribution maintaining a certain distance from the Haar distribution, ensuring better trainability.
  arXiv  Detail & Related papers  (2025-12-07T07:01:44Z)
• Computationally Tractable Offline Quantum Experimental Design for Nuclear Spin Detection [0.0]
  We introduce the surrogate information gain ( SIG) to optimize the selection of data points in the measurements.<n>This approach significantly reduces time requirements in experiments while preserving accuracy in nuclear spin detection.<n>This work also constitutes the first validation of SALI on experimental data.
  arXiv  Detail & Related papers  (2025-08-29T09:29:02Z)
• Hybrid quantum-classical algorithm for near-optimal planning in POMDPs [39.682133213072554]
  Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments.<n>Recent advances demonstrate that inference on sparse Bayesian networks can be accelerated using quantum rejection sampling combined with amplitude amplification.<n>We introduce Quantum Bayesian Reinforcement Learning (QBRL), a hybrid quantum-classical look-ahead algorithm for model-based RL in partially observable environments.
  arXiv  Detail & Related papers  (2025-07-24T17:42:30Z)
• Semi-Implicit Functional Gradient Flow for Efficient Sampling [30.32233517392456]
  We propose a functional gradient ParVI method that uses perturbed particles with Gaussian noise as the approximation family. We show that the corresponding functional gradient flow, which can be estimated via denoising score matching with neural networks, exhibits strong theoretical convergence guarantees. In addition, we present an adaptive version of our method that automatically selects the appropriate noise magnitude during sampling.
  arXiv  Detail & Related papers  (2024-10-23T15:00:30Z)
• QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting [7.945302052915863]
  This paper introduces a novel approach that blends the robustness of this Bayesian technique with the nuanced insights provided by the kernel perspective on quantum models. We incorporate a quantum feature map inspired by Ising interactions and demonstrate its effectiveness in capturing the temporal dependencies critical for precise forecasting.
  arXiv  Detail & Related papers  (2024-08-21T21:39:31Z)
• Poisson Process for Bayesian Optimization [126.51200593377739]
  We propose a ranking-based surrogate model based on the Poisson process and introduce an efficient BO framework, namely Poisson Process Bayesian Optimization (PoPBO) Compared to the classic GP-BO method, our PoPBO has lower costs and better robustness to noise, which is verified by abundant experiments.
  arXiv  Detail & Related papers  (2024-02-05T02:54:50Z)
• Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers [143.6249073384419]
  In this paper, we propose a distributed sampling scheme based on the alternating direction method of multipliers. We provide both theoretical guarantees of our algorithm's convergence and experimental evidence of its superiority to the state-of-the-art. In simulation, we deploy our algorithm on linear and logistic regression tasks and illustrate its fast convergence compared to existing gradient-based methods.
  arXiv  Detail & Related papers  (2024-01-29T02:08:40Z)
• Modeling and Experimental Validation of the Intrinsic SNR in Spin Qubit Gate-Based Readout and Its Impacts on Readout Electronics [0.6531223920356081]
  In semiconductor spin quantum bits (qubits), the radio-frequency (RF) gate-based readout is a promising solution for future large-scale integration. This paper introduces a theoretical framework to evaluate the effect of various parameters, such as the readout probe power, readout chain's noise performance, and integration time on the intrinsic readout signal-to-noise ratio (SNR) By analyzing the underlying physics of spin qubits during readout, this work proposes a qubit readout model that takes into account the qubit's quantum mechanical properties.
  arXiv  Detail & Related papers  (2023-12-14T14:41:12Z)
• Towards a Realistic Model for Cavity-Enhanced Atomic Frequency Comb Quantum Memories [1.0197010747159545]
  Atomic frequency comb (AFC) quantum memory is a favorable protocol in long distance quantum communication. Putting the AFC inside an asymmetric optical cavity enhances the storage efficiency but makes the measurement of the comb properties challenging. We develop a theoretical model for cavity-enhanced AFC quantum memory that includes the effects of dispersion.
  arXiv  Detail & Related papers  (2023-09-19T05:33:01Z)
• A Fast and Stable Marginal-Likelihood Calibration Method with Application to Quantum Characterization [0.0]
  We present a marginal likelihood strategy integrated into the Kennedy-O'Hagan (KOH) Bayesian framework. The proposed method is both computationally efficient and numerically stable, even in large dataset regimes.
  arXiv  Detail & Related papers  (2023-08-24T04:39:18Z)
• Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence [65.63201894457404]
  We propose a novel non-parametric learning paradigm for the identification of drift and diffusion coefficients of non-linear differential equations. The key idea essentially consists of fitting a RKHS-based approximation of the corresponding Fokker-Planck equation to such observations.
  arXiv  Detail & Related papers  (2023-05-24T20:43:47Z)
• Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization [73.80101701431103]
  The linearized-Laplace approximation (LLA) has been shown to be effective and efficient in constructing Bayesian neural networks. We study the usefulness of the LLA in Bayesian optimization and highlight its strong performance and flexibility.
  arXiv  Detail & Related papers  (2023-04-17T14:23:43Z)
• D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
  We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT) We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity. In addition, we show that our approach enables us to explore more complex neural-based wave functions.
  arXiv  Detail & Related papers  (2023-03-01T10:38:10Z)
• Ensemble Reservoir Computing for Dynamical Systems: Prediction of Phase-Space Stable Region for Hadron Storage Rings [0.0]
  Echo State Networks (ESN) are a class of recurrent neural networks that are computationally effective. We present the performance reached by ESN based on the prediction of the phase-space stability region. We observe that the proposed ESN approach is capable of effectively predicting the time evolution of the extent of the dynamic aperture.
  arXiv  Detail & Related papers  (2023-01-17T10:29:07Z)
• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference [59.040209568168436]
  We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights based on the underlying likelihood and prior. This provides (1) a corrected posterior free from network inaccuracies, (2) a performance diagnostic (the sample efficiency) for assessing the proposal and identifying failure cases, and (3) an unbiased estimate of the Bayesian evidence. We carry out a large study analyzing 42 binary black hole mergers observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomHMXP waveform models.
  arXiv  Detail & Related papers  (2022-10-11T18:00:02Z)
• Deep reinforcement learning for quantum multiparameter estimation [0.0]
  We introduce a model-free and deep learning-based approach to implement realistic Bayesian quantum metrology tasks. We prove experimentally the achievement of higher estimation performances than standard methods.
  arXiv  Detail & Related papers  (2022-09-01T18:01:56Z)

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.