Evaluating Supervised Learning Approaches for Quantification of Quantum Entanglement

• URL: http://arxiv.org/abs/2512.21893v1
• Date: Fri, 26 Dec 2025 06:46:07 GMT
• Title: Evaluating Supervised Learning Approaches for Quantification of Quantum Entanglement
• Authors: Shruti Aggarwal, Trasha Gupta, R. K. Agrawal, S. Indu,
• Abstract summary: We study a few machine-learning based models to estimate the amount of entanglement in two-qubit as well as three-qubit systems.<n>Our models predict entanglement without requiring the full state information.
• Score: 1.3066182802188202
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Quantum entanglement is a key resource in quantum computing and quantum information processing tasks. However, its quantification remains a major challenge since it cannot be directly extracted from physical observables. To address this issue, we study a few machine-learning based models to estimate the amount of entanglement in two-qubit as well as three-qubit systems. We use measurement outcomes as the input features and entanglement measures as the training labels. Our models predict entanglement without requiring the full state information. This demonstrates the potential of machine learning as an effcient and powerful tool for characterizing quantum entanglement

Related papers

• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach [3.6384366906530623]
  Quantum steering is an intermediate correlation lying between entanglement and nonlocality.<n>SDP has proven to be a valuable tool to quantify quantum steering.<n>In this work, we utilize the semi-supervised self-training model to estimate the steerable weight.
  arXiv  Detail & Related papers  (2025-01-18T12:14:37Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Quantum Information Processing with Molecular Nanomagnets: an introduction [49.89725935672549]
  We provide an introduction to Quantum Information Processing, focusing on a promising setup for its implementation. We introduce the basic tools to understand and design quantum algorithms, always referring to their actual realization on a molecular spin architecture. We present some examples of quantum algorithms proposed and implemented on a molecular spin qudit hardware.
  arXiv  Detail & Related papers  (2024-05-31T16:43:20Z)
• Exponential quantum advantages in learning quantum observables from classical data [1.9662978733004604]
  We prove quantum advantages for the physically relevant task of learning quantum observables from classical data.<n>Our results shed light on the utility of quantum computers for machine learning problems in the domain of quantum many body physics.
  arXiv  Detail & Related papers  (2024-05-03T11:58:43Z)
• Quantum Process Learning Through Neural Emulation [3.7228085662092845]
  We introduce a neural network that emulates the unknown process by constructing an internal representation of the input ensemble. We show that our model exhibits high accuracy in applications to quantum computing, quantum photonics, and quantum many-body physics.
  arXiv  Detail & Related papers  (2023-08-17T06:53:58Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Scalable Simulation of Quantum Measurement Process with Quantum Computers [13.14263204660076]
  We propose qubit models to emulate the quantum measurement process. One model is motivated by single-photon detection and the other by spin measurement. We generate Schr"odinger cat-like state, and their corresponding quantum circuits are shown explicitly.
  arXiv  Detail & Related papers  (2022-06-28T14:21:43Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Maximal entropy approach for quantum state tomography [3.6344381605841187]
  Current quantum computing devices are noisy intermediate-scale quantum $($NISQ$)$ devices. Quantum tomography tries to reconstruct a quantum system's density matrix by a complete set of observables. We propose an alternative approach to quantum tomography, based on the maximal information entropy, that can predict the values of unknown observables.
  arXiv  Detail & Related papers  (2020-09-02T04:39:45Z)
• Machine learning transfer efficiencies for noisy quantum walks [62.997667081978825]
  We show that the process of finding requirements on both a graph type and a quantum system coherence can be automated. The automation is done by using a convolutional neural network of a particular type that learns to understand with which network and under which coherence requirements quantum advantage is possible. Our results are of importance for demonstration of advantage in quantum experiments and pave the way towards automating scientific research and discoveries.
  arXiv  Detail & Related papers  (2020-01-15T18:36:53Z)

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.