Machine Learning of Quantum Entanglement from Noisy Measurements

• URL: http://arxiv.org/abs/2607.22853v1
• Date: Fri, 24 Jul 2026 18:51:56 GMT
• Title: Machine Learning of Quantum Entanglement from Noisy Measurements
• Abstract summary: We investigate the application of Machine Learning algorithms to the identification and quantitative characterization of quantum entanglement in polarization-entangled photon pairs.<n>The obtained results demonstrate that ML methods can achieve very high classification accuracy, even under extremely limited training conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we investigate the application of Machine Learning (ML) algorithms to the identification and quantitative characterization of quantum entanglement in polarization-entangled photon pairs. The analysis is based on simulated symmetric, informationally complete, positive operator-valued measure (SIC-POVM) measurement data, where each two-qubit state is represented by a 16-dimensional measurement vector corresponding to experimentally accessible coincidence counts. The generated SIC-POVM measurement data include Poissonian shot noise. Several supervised ML algorithms, including Logistic Regression, k-Nearest Neighbors, Decision Trees, Support Vector Machines, and Random Forests, are applied to the classification of separable and entangled states directly from raw measurement data, without explicit density matrix reconstruction or the use of conventional separability criteria. The study additionally explores clustering methods and nonlinear regression techniques for estimating continuous entanglement measures. The obtained results demonstrate that ML methods can achieve very high classification accuracy, even under extremely limited training conditions. These findings indicate that ML may provide an efficient alternative to conventional quantum-state analysis under simulated Poissonian noise conditions.

Related papers

• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements [55.95348868409957]
  We consider the problem of designing a data-driven nonlinear state estimation (DANSE) method that uses (noisy) nonlinear measurements.<n>A recurrent neural network (RNN) provides parameters of a Gaussian prior that characterize the state of the model-free process.<n>The second-order statistics of the state posterior are computed using the nonlinear measurements observed at the time point.
  arXiv  Detail & Related papers  (2025-10-31T14:26:48Z)
• Near-optimal Rank Adaptive Inference of High Dimensional Matrices [46.66027208538566]
  We address the problem of estimating a high-dimensional matrix from linear measurements.<n>We propose an algorithm that combines a Least-Squares estimator with a universal singular value thresholding procedure.<n>Our results rely on an enhanced analysis of matrix denoising methods based on singular value thresholding.
  arXiv  Detail & Related papers  (2025-10-09T12:01:46Z)
• Statistical Signal Processing for Quantum Error Mitigation [12.804941908319792]
  We present a statistical signal processing approach to quantum error mitigation (QEM)<n>Our model assumes that circuit depth is sufficient for depolarizing noise, producing corrupted observations.<n>We show that our method scales to larger qubit counts using synthetically generated data consistent with our noise model.
  arXiv  Detail & Related papers  (2025-05-31T19:34:19Z)
• Pretty-good simulation of all quantum measurements by projective measurements [0.0]
  In quantum theory general measurements are described by so-called Positive Operator-Valued Measures (POVMs)<n>We show that in $d$-dimensional quantum systems an application of depolarizing noise with constant (independent of $d$) visibility parameter makes any POVM simulable by a randomized implementation of projective measurements.<n>This result significantly limits the advantage that POVMs can offer over projective measurements in various information-processing tasks.
  arXiv  Detail & Related papers  (2025-01-16T07:47:24Z)
• Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm [41.25603565852633]
  This work presents an efficient and accurate Bayesian framework for high-dimensional LMMs. The novelty of the approach lies in its partitioning and parameter expansion as well as its fast and scalable computation. A real-world example is provided using data from a study of lupus in children, where we identify genes and clinical factors associated with a new lupus biomarker and predict the biomarker over time.
  arXiv  Detail & Related papers  (2023-10-18T19:34:56Z)
• Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation [45.9982965995401]
  We present Surjective Sequential Neural Likelihood estimation, a novel member in the family of methods for simulation-based inference ( SBI)<n>SSNL fits a dimensionality-reducing surjective normalizing flow model and uses it as a surrogate likelihood function, which allows for computational inference via Markov chain Monte Carlo or variational Bayes methods.<n>We evaluate SSNL on a wide variety of experiments, including two challenging real-world examples from the astrophysics and neuroscience literatures, and show that it either outperforms or is on par with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-08-02T10:02:38Z)
• 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)
• Aspects of scaling and scalability for flow-based sampling of lattice QCD [137.23107300589385]
  Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. It remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations.
  arXiv  Detail & Related papers  (2022-11-14T17:07:37Z)
• Direct parameter estimations from machine-learning enhanced quantum state tomography [3.459382629188014]
  Machine-learning enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. We develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with large Hilbert space, but keep feature extractions with high precision.
  arXiv  Detail & Related papers  (2022-03-30T15:16:02Z)
• Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition [67.95996816744251]
  State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive for practical applications. Current quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of LMs to quantization errors. Novel mixed precision neural network LM quantization methods are proposed in this paper.
  arXiv  Detail & Related papers  (2021-11-29T12:24:02Z)
• Performance of the quantum MaxEnt estimation in the presence of physical symmetries [0.0]
  We study the performance of the MaxEnt method for quantum state estimation when there is prior information about symmetries of the unknown state. We implement this algorithm to carry out numerical simulations estimating the density matrix of several three-qubit states of particular interest for quantum information tasks.
  arXiv  Detail & Related papers  (2021-09-22T15:56:17Z)
• Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection [80.28858481461418]
  Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices. Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices.
  arXiv  Detail & Related papers  (2021-07-11T10:56:24Z)
• Realistic simulation of quantum computation using unitary and measurement channels [1.406995367117218]
  We introduce a new simulation approach that relies on approximating the density matrix evolution by a sum of unitary and measurement channels. This model shows an improvement of at least one order of magnitude in terms of accuracy compared to the best known approaches.
  arXiv  Detail & Related papers  (2020-05-13T14:29:18Z)

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.