The Virtuous Cycle of Quantum-Classical Machine Learning

• URL: http://arxiv.org/abs/2607.10563v1
• Date: Sun, 12 Jul 2026 04:41:10 GMT
• Title: The Virtuous Cycle of Quantum-Classical Machine Learning
• Abstract summary: We argue that both artificial intelligence and quantum computing can benefit substantially from each other.<n>We review the most salient opportunities for machine learning in quantum computing, hoping to inspire a virtuous cycle through which both fields can mutually benefit.
• Score: 2.507756801352061
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence and quantum computing have both seen tremendous progress in recent years, opening up new avenues for accelerating scientific discovery. Classical machine learning has already proven to be useful for addressing challenges in quantum computation; and hardware progress is well underway towards the early fault-tolerant regime. On the other hand, the emerging field of quantum machine learning is aimed at utilizing the strengths of both computational paradigms, via the development of learning algorithms or models that benefit from running on quantum devices or from training on quantum data. We therefore argue that both of these paradigms stand to benefit substantially from each other. Here we review the most salient opportunities for machine learning in quantum computing, hoping to inspire a virtuous cycle through which both fields can mutually benefit.

Related papers

• Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms [50.573955644831386]
  Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, machine learning and quantum computing.<n>It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing.<n>This survey contributes to the existing literature on QeCV with a holistic review of this research field.
  arXiv  Detail & Related papers  (2025-10-08T17:59:51Z)
• Digital quantum simulation of many-body systems: Making the most of intermediate-scale, noisy quantum computers [51.56484100374058]
  This thesis is centered around simulating quantum dynamics on quantum devices.<n>We present an overview of the most relevant quantum algorithms for quantum dynamics.<n>We identify relevant problems within quantum dynamics that could benefit from quantum simulation in the near future.
  arXiv  Detail & Related papers  (2025-08-29T10:37:19Z)
• Digital-Analog Quantum Machine Learning [0.0]
  Machine learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products. dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts.
  arXiv  Detail & Related papers  (2024-11-16T08:54:52Z)
• Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
  Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
  arXiv  Detail & Related papers  (2024-11-14T12:27:50Z)
• The QUATRO Application Suite: Quantum Computing for Models of Human Cognition [49.038807589598285]
  We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling. We release QUATRO, a collection of quantum computing applications from cognitive models.
  arXiv  Detail & Related papers  (2023-09-01T17:34:53Z)
• 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)
• 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)
• New Trends in Quantum Machine Learning [0.0]
  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.
  arXiv  Detail & Related papers  (2021-08-22T08:23:30Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)
• 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)

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.