Simulating Quantum Materials with Digital Quantum Computers

• URL: http://arxiv.org/abs/2101.08836v2
• Date: Mon, 1 Feb 2021 20:00:34 GMT
• Title: Simulating Quantum Materials with Digital Quantum Computers
• Authors: Lindsay Bassman, Miroslav Urbanek, Mekena Metcalf, Jonathan Carter, Alexander F. Kemper, Wibe de Jong
• Abstract summary: 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.
• Score: 55.41644538483948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum materials exhibit a wide array of exotic phenomena and practically useful properties. A better understanding of these materials can provide deeper insights into fundamental physics in the quantum realm as well as advance technology for entertainment, healthcare, and sustainability. The emergence of digital quantum computers (DQCs), which can efficiently perform quantum simulations that are otherwise intractable on classical computers, provides a promising path forward for testing and analyzing the remarkable, and often counter-intuitive, behavior of quantum materials. Equipped with these new tools, scientists from diverse domains are racing towards achieving physical quantum advantage (i.e., using a quantum computer to learn new physics with a computation that cannot feasibly be run on any classical computer). The aim of this review, therefore, is to provide a summary of progress made towards this goal that is accessible to scientists across the physical sciences. We will first review the available technology and algorithms, and detail the myriad ways to represent materials on quantum computers. Next, we will showcase the simulations that have been successfully performed on currently available DQCs, emphasizing the variety of properties, both static and dynamic, that can be studied with this nascent technology. Finally, we work through two examples of how to map a materials problem onto a DQC, with full code included in the Supplementary Material. It is our hope that this review can serve as an organized overview of progress in the field for domain experts and an accessible introduction to scientists in related fields interested in beginning to perform their own simulations of quantum materials on DQCs.

Related papers

• Quantum algorithms for scientific computing [0.0]
  Areas that are likely to have the greatest impact on high performance computing include simulation of quantum systems, optimization, and machine learning. Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, aerospace engineering, and the design of "green" materials for sustainable development.
  arXiv  Detail & Related papers  (2023-12-22T18:29:31Z)
• 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)
• 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)
• Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools [5.557009030881896]
  We conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing. We discuss the current essence, identify open challenges and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
  arXiv  Detail & Related papers  (2022-04-04T21:21:12Z)
• Recompilation-enhanced simulation of electron-phonon dynamics on IBM Quantum computers [62.997667081978825]
  We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems. We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling. Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
  arXiv  Detail & Related papers  (2022-02-16T19:00:00Z)
• 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)
• Quantum Embedding Theories to Simulate Condensed Systems on Quantum Computers [0.6299766708197883]
  Quantum computers hold promise to improve the efficiency of quantum simulations of materials. These are promising systems to build future quantum technologies, e.g., computers, sensors and devices for quantum communications. Although quantum simulations on quantum architectures are in their infancy, promising results for realistic systems appear to be within reach.
  arXiv  Detail & Related papers  (2021-05-11T01:12:46Z)
• 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)
• 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.