Issue |
EPJ Web of Conf.
Volume 284, 2023
15th International Conference on Nuclear Data for Science and Technology (ND2022)
|
|
---|---|---|
Article Number | 16002 | |
Number of page(s) | 3 | |
Section | Computational Techniques and Machine Learning | |
DOI | https://doi.org/10.1051/epjconf/202328416002 | |
Published online | 26 May 2023 |
https://doi.org/10.1051/epjconf/202328416002
Variance minimisation on a quantum computer of the Lipkin-Meshkov-Glick model with three particles
1 Department of Physics, University of Surrey, Guildford, GU2 7XH, UK
2 AWE, Aldermaston, Reading, RG7 4PR, UK
* e-mail: i.hobday@surrey.ac.uk
** e-mail: p.stevenson@surrey.ac.uk
Published online: 26 May 2023
Quantum computing opens up new possibilities for the simulation of many-body nuclear systems. As the number of particles in a many-body system increases, the size of the space if the associated Hamiltonian increases exponentially. This presents a challenge when performing calculations on large systems when using classical computing methods. By using a quantum computer, one may be able to overcome this difficulty thanks to the exponential way the Hilbert space of a quantum computer grows with the number of quantum bits (qubits). Our aim is to develop quantum computing algorithms which can reproduce and predict nuclear structure such as level schemes and level densities. As a sample Hamiltonian, we use the Lipkin-Meshkov-Glick model. We use an efficient encoding of the Hamiltonian onto many-qubit systems, and have developed an algorithm allowing the full excitation spectrum of a nucleus to be determined with a variational algorithm capable of implementation on today’s quantum computers with a limited number of qubits. Our algorithm uses the variance of the Hamiltonian,⟨H⟩2 − ⟨H⟩2, as a cost function for the widely-used variational quantum eigensolver (VQE). In this work we present a variance based method of finding the excited state spectrum of a small nuclear system using a quantum computer, using a reduced-qubit encoding method.
© The Authors, published by EDP Sciences, 2023
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