Is quantum advantage the right goal for quantum machine learning?

• URL: http://arxiv.org/abs/2203.01340v2
• Date: Wed, 8 Feb 2023 09:57:18 GMT
• Title: Is quantum advantage the right goal for quantum machine learning?
• Authors: Maria Schuld, Nathan Killoran
• Abstract summary: We argue that it is difficult to say something about the practical power of quantum computers for machine learning with the tools we are currently using. We argue that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: Today's machine learning algorithms are notoriously powerful in practice, but remain theoretically difficult to study. Quantum computing, in contrast, does not offer practical benchmarks on realistic scales, and theory is the main tool we have to judge whether it could become relevant for a problem. In this perspective we explain why it is so difficult to say something about the practical power of quantum computers for machine learning with the tools we are currently using. We argue that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and highlight examples for how other perspectives in existing research provide an important alternative to the focus on advantage.

Related papers

• 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)
• The Quantum Tortoise and the Classical Hare: A simple framework for understanding which problems quantum computing will accelerate (and which it will not) [1.3812010983144802]
  Quantum computing promises transformational gains for solving some problems, but little to none for others. Our analysis reveals that many problems, particularly those of small to moderate size that can be important for typical businesses, will not benefit from quantum computing. Since very large algorithmic gains are rare in practice and theorized to be rare even in principle, our analysis suggests that the benefits from quantum computing will flow either to users of these rare cases, or practitioners processing very large data.
  arXiv  Detail & Related papers  (2023-10-24T04:20:10Z)
• Reliable AI: Does the Next Generation Require Quantum Computing? [71.84486326350338]
  We show that digital hardware is inherently constrained in solving problems about optimization, deep learning, or differential equations. In contrast, analog computing models, such as the Blum-Shub-Smale machine, exhibit the potential to surmount these limitations.
  arXiv  Detail & Related papers  (2023-07-03T19:10:45Z)
• Relation between quantum advantage in supervised learning and quantum computational advantage [0.0]
  Recent work shows that computational and learning advantage are, in general, not equivalent. The existence of efficient algorithms to generate training sets emerges as the cornerstone of such conditions. Results are applied to prove that there is a quantum speed-up for some learning tasks based on the prime factorization problem.
  arXiv  Detail & Related papers  (2023-04-13T17:34:53Z)
• 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)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• 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)
• Systematic Literature Review: Quantum Machine Learning and its applications [0.0]
  This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023. This study identified 94 articles that used quantum machine learning techniques and algorithms. An improvement in the quantum hardware is required since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.
  arXiv  Detail & Related papers  (2022-01-11T17:36:34Z)
• Quantum Machine Learning: Fad or Future? [0.0]
  We're fast approach the threshold of the maximum possible computational capacity available to us by the means of classical computing devices. This is due to the exponential increase in model sizes which now have parameters in the magnitude of billions and trillions. This paper will look forth to test and verify the aspects in which quantum machine learning can help improve over classical machine learning approaches.
  arXiv  Detail & Related papers  (2021-06-20T15:39:36Z)
• 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.