Physics-Based versus Data-Driven Classification of Single-Photon Quantum Emitters from Sparse Autocorrelation Data

• URL: http://arxiv.org/abs/2608.19528v1
• Date: Thu, 20 Aug 2026 00:53:49 GMT
• Title: Physics-Based versus Data-Driven Classification of Single-Photon Quantum Emitters from Sparse Autocorrelation Data
• Abstract summary: We introduce sequential Bayesian inference for single photon emitter classification.<n>We benchmark it against Levenberg-Marquardt fitting and a feedforward neural network.<n>All three approaches achieve high, near-perfect accuracy with sufficient integration time.
• Score: 0.1759008116536278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying single photon emitters from large, inhomogeneous candidate populations is key to realizing many quantum applications. This requires measuring the emitters second order autocorrelation function, whose statistical reliability is fundamentally limited by acquisition time. Machine learning classifiers can accelerate identification from sparse data, but their performance relative to physics-based inference has not been systematically examined. Here, we introduce sequential Bayesian inference for single photon emitter classification and benchmark it against Levenberg-Marquardt fitting and a feedforward neural network. We use synthetic training and test data calibrated against real Hanbury Brown-Twiss measurements from hexagonal boron nitride emitters, enabling evaluation against an exactly known ground-truth emitter number under realistic noise and background conditions. All three approaches achieve high, near-perfect accuracy with sufficient integration time, but differ greatly in convergence rate and robustness under sparse photon statistics. The neural network is most robust at short integration times. The Bayesian classifier reaches near-perfect accuracy fastest, while retaining full physical interpretability. Levenberg-Marquardt fitting remains a valuable, fully interpretable method, achieving the highest recall despite being the slowest to converge. These results lead to several key conclusions. No single method dominates across all performance metrics. Relying on any one metric alone can give a misleading picture of classifier performance, particularly under sparse photon statistics. Physics-based and data-driven methods are complementary rather than competing approaches. Together, these findings provide practical guidance for selecting and combining classification strategies for scalable single-photon-source screening and other quantum-emitter characterization tasks.

Related papers

• Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation [48.887627688666974]
  Learning dissipation rates in large-scale open quantum systems is a major obstacle for near-term quantum technologies.<n>Here, we propose a scalable noise-learning framework for Lindblad dissipation rates that combines a simulation method, the Jump Method (TJM)<n>We demonstrate the approach on two noise models in the Ising model, in which independent dissipation rates are learned for each site up to $N_mathrmsite=16$.<n>This combination of scalable numerics and rigorous theoretical guarantees positions TJM-based noise learning as a practical foundation for characterizing dissipation in large quantum devices.
  arXiv  Detail & Related papers  (2026-08-25T15:07:11Z)
• Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation [55.47411235954028]
  We recast imputation under missing completely at random as density estimation from masked observations.<n>We obtain a convex empirical risk problem with closed form marginals, solvable by a Newton interior point method.<n>The resulting PSD Impute model yields both single and multiple imputations from the same fitted density.
  arXiv  Detail & Related papers  (2026-07-08T15:51:05Z)
• EventShiftFlow: Towards Hardware-efficient FPGA-based Flow Estimation [43.77587889970362]
  Event-based vision sensors offer asynchronous, high-temporal-resolution measurements.<n>Many event-based motion estimation methods are computationally intensive and difficult to map to FPGA hardware.<n>We present a streaming velocity estimator that discretizes asynchronous events into fixed-duration time bins, constructs a 1-bit spatial occupancy grid, and evaluates multiple velocity hypotheses in parallel.
  arXiv  Detail & Related papers  (2026-05-27T11:13:58Z)
• Unlocking photodetection for quantum sensing with Bayesian likelihood-free methods and deep learning [0.0]
  In photodetection, the key challenge is the fast interpretation of click-patterns that exhibit non-classical statistics.<n>We achieve this goal by comparing Bayesian likelihood-free methods with ones based on deep learning (DL)<n>Our results pave the way for dynamical control of quantum sensors that leverage non-classical effects in photodetection.
  arXiv  Detail & Related papers  (2026-02-23T12:47:41Z)
• AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors [16.821447184897142]
  We report an AI-accelerated Bayesian inference method for fluorescence readout in neutral atom arrays.<n>Our approach leverages Bayesian inference to achieve reliable state detection at the single-photon level under short exposure.<n>This framework supports scalable, real-time readout of large atom arrays and paves the way toward AI-enhanced quantum technology in computation and sensing.
  arXiv  Detail & Related papers  (2025-12-24T03:51:56Z)
• Continuous-variable photonic quantum extreme learning machines for fast collider-data selection [0.0]
  We study continuous-variable photonic quantum extreme learning machines as fast, low-overhead front-ends for collider data processing.<n>Data is encoded in photonic modes through quadrature displacements and propagated through a fixed-time Gaussian quantum substrate.<n>Only a linear classifier is trained, using a single linear solve, so retraining is fast and the optical path and detector response set the analytical and inference latency.
  arXiv  Detail & Related papers  (2025-10-15T18:21:32Z)
• Neural Network Enhanced Single-Photon Fock State Tomography [6.434126816101052]
  We report the experimental implementation of single-photon quantum state tomography by directly estimating target parameters. Our neural network enhanced quantum state tomography characterizes the photon number distribution for all possible photon number states from the balanced homodyne detectors. Such a fast, robust, and precise quantum state tomography provides us a crucial diagnostic toolbox for the applications with single-photon Fock states and other non-Gaussisan quantum states.
  arXiv  Detail & Related papers  (2024-05-05T04:58:18Z)
• Efficient characterization of blinking quantum emitters from scarce data sets via machine learning [0.0]
  Single photon emitters universally display fluorescence intermittency or photoblinking. We present a multi-feature regression algorithm and a genetic algorithm that allow for the extraction of blinking on/off rates with >85% accuracy. Our algorithms effectively extend the range of surveyable blinking systems and trapping dynamics to those that would otherwise be considered too short-lived to be investigated.
  arXiv  Detail & Related papers  (2023-08-24T18:51:30Z)
• Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast [39.90150176899222]
  Exoplanet signals can only be identified when combining several observations with dedicated detection algorithms. We learn a model of the spatial, temporal and spectral characteristics of the nuisance, directly from the observations. A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources.
  arXiv  Detail & Related papers  (2023-06-21T13:42:07Z)
• 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)
• FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels [82.53569355337586]
  This work offers an efficient solution to temporal point processes inference using general parametric kernels with finite support. The method's effectiveness is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG) Results show that the proposed approach leads to an improved estimation of pattern latency than the state-of-the-art.
  arXiv  Detail & Related papers  (2022-10-10T12:35:02Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity [57.275753974812666]
  Federated learning involves learning from data samples distributed across a network of clients while the data remains local. In this paper, we propose a novel straggler-resilient federated learning method that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure.
  arXiv  Detail & Related papers  (2020-12-28T19:21:14Z)

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.