Quantum Computing Methods for Supervised Learning
- URL: http://arxiv.org/abs/2006.12025v1
- Date: Mon, 22 Jun 2020 06:34:42 GMT
- Title: Quantum Computing Methods for Supervised Learning
- Authors: Viraj Kulkarni, Milind Kulkarni, Aniruddha Pant
- Abstract summary: 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.
- Score: 0.08594140167290096
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The last two decades have seen an explosive growth in the theory and practice
of both quantum computing and machine learning. Modern machine learning systems
process huge volumes of data and demand massive computational power. As silicon
semiconductor miniaturization approaches its physics limits, quantum computing
is increasingly being considered to cater to these computational needs in the
future. Small-scale quantum computers and quantum annealers have been built and
are already being sold commercially. Quantum computers can benefit machine
learning research and application across all science and engineering domains.
However, owing to its roots in quantum mechanics, research in this field has so
far been confined within the purview of the physics community, and most work is
not easily accessible to researchers from other disciplines. In this paper, we
provide a background and summarize key results of quantum computing before
exploring its application to supervised machine learning problems. By eschewing
results from physics that have little bearing on quantum computation, we hope
to make this introduction accessible to data scientists, machine learning
practitioners, and researchers from across disciplines.
Related papers
- Quantum Computing: Vision and Challenges [16.50566018023275]
We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers.
Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate.
arXiv Detail & Related papers (2024-03-04T17:33:18Z) - 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) - 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) - Standard Model Physics and the Digital Quantum Revolution: Thoughts
about the Interface [68.8204255655161]
Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress.
From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
arXiv Detail & Related papers (2021-07-10T06:12:06Z) - 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) - Quantum Computation [0.0]
We will discuss and summarized the core principles and practical application areas of quantum computation.
The mapping of computation onto the behavior of physical systems is a historical challenge.
We will evaluate the essential technology required for quantum computers to be able to function correctly.
arXiv Detail & Related papers (2020-06-04T11:57:18Z) - Quantum Machine Learning in High Energy Physics [1.191194620421783]
This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics.
An interesting question is whether there are ways to apply quantum machine learning to High Energy Physics.
arXiv Detail & Related papers (2020-05-18T10:48:39Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z) - Quantum algorithms for quantum chemistry and quantum materials science [2.867517731896504]
We briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics, that are of potential interest for solution on a quantum computer.
We take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal state simulation, and analyze their strengths and weaknesses for future developments.
arXiv Detail & Related papers (2020-01-10T22:49:56Z)
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.