Predicting excited states from ground state wavefunction by supervised quantum machine learning

• URL: http://arxiv.org/abs/2002.12925v4
• Date: Wed, 4 Nov 2020 03:10:35 GMT
• Title: Predicting excited states from ground state wavefunction by supervised quantum machine learning
• Authors: Hiroki Kawai and Yuya O. Nakagawa
• Abstract summary: Scheme of supervised quantum machine learning predicts excited-state properties of molecules only from their ground state wavefunction. Our contribution will enhance the applications of quantum computers in the study of quantum chemistry and quantum materials.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Excited states of molecules lie in the heart of photochemistry and chemical reactions. The recent development in quantum computational chemistry leads to inventions of a variety of algorithms that calculate the excited states of molecules on near-term quantum computers, but they require more computational burdens than the algorithms for calculating the ground states. In this study, we propose a scheme of supervised quantum machine learning which predicts the excited-state properties of molecules only from their ground state wavefunction resulting in reducing the computational cost for calculating the excited states. Our model is comprised of a quantum reservoir and a classical machine learning unit which processes the measurement results of single-qubit Pauli operators with the output state from the reservoir. The quantum reservoir effectively transforms the single-qubit operators into complicated multi-qubit ones which contain essential information of the system, so that the classical machine learning unit may decode them appropriately. The number of runs for quantum computers is saved by training only the classical machine learning unit, and the whole model requires modest resources of quantum hardware that may be implemented in current experiments. We illustrate the predictive ability of our model by numerical simulations for small molecules with and without noise inevitable in near-term quantum computers. The results show that our scheme well reproduces the first and second excitation energies as well as the transition dipole moment between the ground states and excited states only from the ground state as an input. We expect our contribution will enhance the applications of quantum computers in the study of quantum chemistry and quantum materials.

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)
• 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)
• 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)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)
• Quantum walk processes in quantum devices [55.41644538483948]
  We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
  arXiv  Detail & Related papers  (2020-12-28T18:04:16Z)
• Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices [0.8206877486958002]
  Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning. All these quantum machine learning approaches are experimentally feasible and effective on the state-of-the-art quantum devices.
  arXiv  Detail & Related papers  (2020-11-10T04:45:52Z)
• How will quantum computers provide an industrially relevant computational advantage in quantum chemistry? [0.0]
  We go over subtle complications of quantum chemical research that tend to be overlooked in discussions involving quantum computers. We estimate quantum computer resources that will be required for performing calculations on quantum computers with chemical accuracy for several types of molecules.
  arXiv  Detail & Related papers  (2020-09-25T23:21:16Z)
• Quantum simulations of materials on near-term quantum computers [1.856334276134661]
  We present a quantum embedding theory for the calculation of strongly-correlated electronic states of active regions. We demonstrate the accuracy and effectiveness of the approach by investigating several defect quantum bits in semiconductors.
  arXiv  Detail & Related papers  (2020-02-25T20:57:57Z)
• 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.