Modern applications of machine learning in quantum sciences

• URL: http://arxiv.org/abs/2204.04198v3
• Date: Wed, 15 Nov 2023 21:36:17 GMT
• Title: Modern applications of machine learning in quantum sciences
• Authors: Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch, Marcin P{\l}odzie\'n, Kaelan Donatella, Kim A. Nicoli, Paolo Stornati, Rouven Koch, Miriam B\"uttner, Robert Oku{\l}a, Gorka Mu\~noz-Gil, Rodrigo A. Vargas-Hern\'andez, Alba Cervera-Lierta, Juan Carrasquilla, Vedran Dunjko, Marylou Gabri\'e, Patrick Huembeli, Evert van Nieuwenburg, Filippo Vicentini, Lei Wang, Sebastian J. Wetzel, Giuseppe Carleo, Eli\v{s}ka Greplov\'a, Roman Krems, Florian Marquardt, Micha{\l} Tomza, Maciej Lewenstein, Alexandre Dauphin
• Abstract summary: We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms. We discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
• Score: 51.09906911582811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.

Related papers

• Large-scale quantum reservoir learning with an analog quantum computer [45.21335836399935]
  We develop a quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning.
  arXiv  Detail & Related papers  (2024-07-02T18:00:00Z)
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance [8.050429258747256]
  We offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.
  arXiv  Detail & Related papers  (2024-01-21T00:19:16Z)
• 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)
• Variational quantum algorithms for machine learning: theory and applications [0.0]
  This thesis provides a comprehensive review of the state-of-the-art in the field of Variational Quantum Algorithms and Quantum Machine Learning. The discussion then shifts to quantum machine learning, where an introduction to the elements of machine learning and statistical learning theory is followed by a review of the most common quantum counterparts of machine learning models.
  arXiv  Detail & Related papers  (2023-06-16T17:28:35Z)
• 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)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Quantum machine learning beyond kernel methods [0.0]
  We show that parametrized quantum circuit models can exhibit a critically better generalization performance than their kernel formulations. Our results constitute another step towards a more comprehensive theory of quantum machine learning models next to kernel formulations.
  arXiv  Detail & Related papers  (2021-10-25T18:00:02Z)
• Quantum Geometric Machine Learning for Quantum Circuits and Control [78.50747042819503]
  We review and extend the application of deep learning to quantum geometric control problems. We demonstrate enhancements in time-optimal control in the context of quantum circuit synthesis problems. Our results are of interest to researchers in quantum control and quantum information theory seeking to combine machine learning and geometric techniques for time-optimal control problems.
  arXiv  Detail & Related papers  (2020-06-19T19:12:14Z)
• Machine Learning for Quantum Matter [0.0]
  We review the recent development and adaptation of machine learning ideas for the purpose advancing research in quantum matter. We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics.
  arXiv  Detail & Related papers  (2020-03-24T18:00:30Z)

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.