ArXiv TLDR

A minimal implementation of Yang-Mills theory on a digital quantum computer

🐦 Tweet
2604.15132

Georg Bergner, Masanori Hanada, Emanuele Mendicelli

hep-lathep-thnucl-thquant-ph

TLDR

This paper presents a minimal implementation of Yang-Mills theory for digital quantum simulation, simplifying Hamiltonians and reducing resource needs.

Key contributions

  • Introduces simplified Hamiltonians for SU(N) Yang-Mills theory on digital quantum computers.
  • Develops methods to improve convergence to the infinite mass limit, removing large scalar mass requirements.
  • Reduces resource needs for SU(2) theory by leveraging its embedding into R^4.

Why it matters

This paper significantly advances the quantum simulation of non-Abelian gauge theories, crucial for understanding fundamental physics. By simplifying Hamiltonians and reducing resource requirements, it brings quantum advantage closer for complex simulations.

Original Abstract

We present a minimal implementation of SU($N$) pure Yang-Mills theory in $3+1$ dimensions for digital quantum simulation, designed to enable quantum advantage. Building on the orbifold lattice simulation protocol with logarithmic scaling in the local Hilbert-space truncation, we introduce further simplified Hamiltonians. Furthermore, we test simple methods that improve the convergence to the infinite mass limit, thereby removing the requirement of a large scalar mass to obtain the Kogut-Susskind Hamiltonian. For the SU(2) theory, we can cut the resource requirement further by utilizing the embedding of $\mathrm{SU}(2)\cong\mathrm{S}^3$ into $\mathbb{R}^4$. Monte Carlo simulations of the Euclidean path integral were used to benchmark the accuracy of these new analytical improvements to the theory. These results provide further support for the noncompact-variable-based approach as a practical framework for quantum simulation of non-Abelian gauge theories.

📬 Weekly AI Paper Digest

Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.