New Trends in Quantum Machine Learning

• URL: http://arxiv.org/abs/2108.09664v1
• Date: Sun, 22 Aug 2021 08:23:30 GMT
• Title: New Trends in Quantum Machine Learning
• Authors: Lorenzo Buffoni and Filippo Caruso
• Abstract summary: We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms. Data visualization techniques and other schemes borrowed from machine learning can be of great use to theoreticians.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Here we will give a perspective on new possible interplays between Machine Learning and Quantum Physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by breakthroughs in physical hardware, as well as to improve existing models or devise new learning schemes in the quantum domain. Moreover, there are lots of experiments in quantum physics that do generate incredible amounts of data and machine learning would be a great tool to analyze those and make predictions, or even control the experiment itself. On top of that, data visualization techniques and other schemes borrowed from machine learning can be of great use to theoreticians to have better intuition on the structure of complex manifolds or to make predictions on theoretical models. This new research field, named as Quantum Machine Learning, is very rapidly growing since it is expected to provide huge advantages over its classical counterpart and deeper investigations are timely needed since they can be already tested on the already commercially available quantum machines.

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)
• Symmetry-invariant quantum machine learning force fields [0.0]
  We design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning.
  arXiv  Detail & Related papers  (2023-11-19T16:15:53Z)
• 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)
• Artificial Intelligence and Machine Learning for Quantum Technologies [6.25426839308312]
  We showcase in illustrative examples how scientists in the past few years have started to use machine learning to analyze quantum measurements. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.
  arXiv  Detail & Related papers  (2022-08-07T23:02:55Z)
• 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)
• Modern applications of machine learning in quantum sciences [51.09906911582811]
  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.
  arXiv  Detail & Related papers  (2022-04-08T17:48:59Z)
• Simulating Quantum Materials with Digital Quantum Computers [55.41644538483948]
  Digital quantum computers (DQCs) can efficiently perform quantum simulations that are otherwise intractable on classical computers. The aim of this review is to provide a summary of progress made towards achieving physical quantum advantage.
  arXiv  Detail & Related papers  (2021-01-21T20:10:38Z)
• Power of data in quantum machine learning [2.1012068875084964]
  We show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. We propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime.
  arXiv  Detail & Related papers  (2020-11-03T19:00:01Z)
• Quantum Computing Methods for Supervised Learning [0.08594140167290096]
  Small-scale quantum computers and quantum annealers have been built and are already being sold commercially. We provide a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems.
  arXiv  Detail & Related papers  (2020-06-22T06:34:42Z)
• 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.