PaperPanorama

HEP Lattice·hep-lat

Fri·Aug 28, 2026

2 papers—1 primary·1 cross-listed

All2Primary1Cross-lists1
  1. 01

    Efficient Quantum Simulations of Yang-Mills theory with Maximal-tree Gauge

    Tianyin Li🇯🇵 · Ying-Ying Li🇨🇳 · Xiaoyang Wang🇯🇵 · Hongxi Xing🇨🇳

    We develop a quantum algorithmic framework for the efficient simulation of Yang--Mills theories, including the gauge theory in Quantum Chromodynamics (QCD). The framework uses maximal-tree gauge in terms of gauge field variables that removes all local gauge redundancies. In the resulting gauge-fixed formulation and digitization in the field-amplitude basis, we show that Hamiltonian time evolution admits an efficient implementation based on quantum singular value transformation (QSVT). We derive upper bounds on the total number of qubits and gate complexity, finding polynomial scaling with the inverse simulation precision , lattice volume , gauge coupling , and target energy scale . Our results provide a rigorous complexity-theoretic demonstration that non-Abelian Yang--Mills theories can be simulated efficiently on quantum computers, paving the way toward first-principles quantum simulations of non-perturbative QCD dynamics.

    hep-lathep-phquant-ph0 citations
  2. 02

    Spectral Fingerprints of Gauge Theories on a Quantum Computer

    Graham Van Goffrier🇬🇧 · Debasish Banerjee🇬🇧 · Bipasha Chakraborty🇬🇧 · Emilie Huffman🇺🇸

    Maximally mixed state spectral sampling is an unbiased quantum algorithm that allows for extraction of a finite-resolution spectral distribution from a Hamiltonian over potentially the entire allowed range of energies. We show how it may be focused on any desired area of the spectrum in order to learn about the full \textit{fingerprint} of the model of interest: from its ground state phenomena such as quantum criticality, obtained from the lowest lying energies, to its thermalization behavior, obtained from the mid-spectrum. We demonstrate this technique specifically on a non-Abelian gauge theory, providing a comprehensive analysis of the steps necessary for performing this algorithm, as well as what is possible in the near-term with superconducting quantum hardware, performing simulations with circuits that are two-qubit gates deep. We show how this algorithm is able to take advantage of emerging dynamical circuit capabilities in near-term hardware to roughly halve the number required qubits, as well as how quantum readout error mitigation is trivial for this method. Along the way, we propose a novel strategy for compiling the controlled-time evolutions needed for spectral sampling by means of Pauli-frame optimizations. We illustrate two physical applications of quantum spectral sampling -- disordered many-body transitions, and mid-spectrum densities of states -- and what postprocessing steps they require beyond the Fourier outputs of the algorithm.

    ↳ quant-phcond-mat.str-elhep-lathep-th0 citations

Affiliations

first authorsco-authorsvia INSPIRE