Implementation of Quantum Machine Learning for Electronic Structure Calculations of Periodic Systems on Quantum Computing Devices

• URL: http://arxiv.org/abs/2103.02037v2
• Date: Fri, 28 May 2021 22:20:00 GMT
• Title: Implementation of Quantum Machine Learning for Electronic Structure Calculations of Periodic Systems on Quantum Computing Devices
• Authors: Shree Hari Sureshbabu, Manas Sajjan, Sangchul Oh, Sabre Kais
• Abstract summary: We implement the benchmark test of the hybrid quantum machine learning on the IBM-Q quantum computer. This benchmark result implies that the hybrid quantum machine learning, empowered by quantum computers, could provide a new way of calculating the electronic structures of quantum many-body systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum machine learning algorithms, the extensions of machine learning to quantum regimes, are believed to be more powerful as they leverage the power of quantum properties. Quantum machine learning methods have been employed to solve quantum many-body systems and have demonstrated accurate electronic structure calculations of lattice models, molecular systems, and recently periodic systems. A hybrid approach using restricted Boltzmann machines and a quantum algorithm to obtain the probability distribution that can be optimized classically is a promising method due to its efficiency and ease of implementation. Here we implement the benchmark test of the hybrid quantum machine learning on the IBM-Q quantum computer to calculate the electronic structure of typical 2-dimensional crystal structures: hexagonal-Boron Nitride and graphene. The band structures of these systems calculated using the hybrid quantum machine learning are in good agreement with those obtained by the conventional electronic structure calculation. This benchmark result implies that the hybrid quantum machine learning, empowered by quantum computers, could provide a new way of calculating the electronic structures of quantum many-body systems.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Electronic Structure Calculations using Quantum Computing [0.0]
  We present a hybrid Classical-Quantum computational procedure that uses the Variational Quantum Eigensolver (VQE) algorithm. Our algorithm offers a streamlined process requiring fewer computational resources than classical methods. Results indicate the potential of the algorithm to expedite the development of new materials and technologies.
  arXiv  Detail & Related papers  (2023-05-13T12:02:05Z)
• 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)
• Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
  We design, implement, and evaluate three hybrid quantum k-Means algorithms. We exploit quantum phenomena to speed up the computation of distances. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
  arXiv  Detail & Related papers  (2022-12-13T16:04:16Z)
• Extending the reach of quantum computing for materials science with machine learning potentials [0.3352108528371308]
  We propose a strategy to extend the scope of quantum computational methods to large scale simulations using a machine learning potential. We investigate the trainability of a machine learning potential selecting various sources of noise. We construct the first machine learning potential from data computed on actual IBM Quantum processors for a hydrogen molecule.
  arXiv  Detail & Related papers  (2022-03-14T15:59:30Z)
• 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)
• Emerging quantum computing algorithms for quantum chemistry [0.0]
  Digital quantum computers provide a computational framework for solving the Schr"odinger equation. Quantum computing algorithms for the quantum simulation of these systems have recently witnessed remarkable growth.
  arXiv  Detail & Related papers  (2021-09-07T05:18:25Z)
• Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation [58.720142291102135]
  We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer. We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
  arXiv  Detail & Related papers  (2021-06-22T18:00:00Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)
• Quantum ensemble of trained classifiers [2.048335092363436]
  A quantum computer is capable of representing an exponentially large set of states, according to the number of qubits available. Quantum machine learning explores the potential of quantum computing to enhance machine learning algorithms.
  arXiv  Detail & Related papers  (2020-07-18T01:01:33Z)
• Quantum computation of silicon electronic band structure [0.0]
  We show that unprecedented methods used in quantum chemistry, designed to simulate molecules on quantum processors, can be extended to calculate properties of periodic solids. In particular, we present minimal depth circuits implementing the variational quantum eigensolver algorithm and successfully use it to compute the band structure of silicon on a quantum machine for the first time.
  arXiv  Detail & Related papers  (2020-06-06T07:45:06Z)

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.