English

Towards a variational Jordan-Lee-Preskill quantum algorithm

Quantum Physics 2022-12-29 v4 Information Theory Machine Learning High Energy Physics - Theory math.IT

Abstract

Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices. In this work, we formulate the theory of (time-dependent) variational quantum simulation of the 1+1 dimensional λϕ4\lambda \phi^4 quantum field theory including encoding, state preparation, and time evolution, with several numerical simulation results. These algorithms could be understood as near-term variational quantum circuit (quantum neural network) analogs of the Jordan-Lee-Preskill algorithm, the basic algorithm for simulating quantum field theory using universal quantum devices. Besides, we highlight the advantages of encoding with harmonic oscillator basis based on the LSZ reduction formula and several computational efficiency such as when implementing a bosonic version of the unitary coupled cluster ansatz to prepare initial states. We also discuss how to circumvent the "spectral crowding" problem in the quantum field theory simulation and appraise our algorithm by both state and subspace fidelities.

Keywords

Cite

@article{arxiv.2109.05547,
  title  = {Towards a variational Jordan-Lee-Preskill quantum algorithm},
  author = {Junyu Liu and Zimu Li and Han Zheng and Xiao Yuan and Jinzhao Sun},
  journal= {arXiv preprint arXiv:2109.05547},
  year   = {2022}
}

Comments

v2: modified style, add references, clear typos. v3; v4: significant change, authors added

R2 v1 2026-06-24T05:53:43.836Z