Gauss law codes identify the full gauge-invariant sector as the code space while vacuum codes restrict to the matter vacuum, with the two shown to be unitarily equivalent for finite gauge groups.
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First tensor-network simulation of real-time hadronic scattering in (1+1)D SU(2) lattice gauge theory reveals entanglement and spatial delocalization in the baryon-number-one sector at strong coupling.
A lattice discretization of constant modes in 2+1D Maxwell-Chern-Simons theory on a torus maps to a generalized Harper-Hofstadter model, reproducing continuum topological degeneracy under specific commensurability conditions with truncation convergence analyzed.
Diagonalizing field operators before Pauli-string decomposition exponentially cuts circuit depth and Trotter errors in 2+1D scalar QFT simulations, with faster local-truncation convergence for Lorentzian energy-energy correlators than the Jordan-Lee-Preskill amplitude-basis method.
New analytic and Monte Carlo-assisted method tightens energy-based boson truncation bounds, reducing volume dependence in (1+1)D scalar and (2+1)D U(1) gauge theories.
Quantum simulation on IBM Nighthawk extracts attractive kink-antikink potential in large-N QCD2 mapped to XXZ chain, agreeing with exact diagonalization benchmarks.
Quantum advantage in hadronic tomography should be evaluated selectively for CFFs, GPDs, TMDs, and GTMDs because their light-front and real-time correlation functions create ill-posed inverse problems that quantum algorithms may address at algorithmic, computational, and inference levels.
The talk summarizes the quantum simulation program for lattice gauge theories, covering target problems in dense matter, algorithmic strategies, recent progress, and remaining challenges.
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Gauss law codes and vacuum codes from lattice gauge theories
Gauss law codes identify the full gauge-invariant sector as the code space while vacuum codes restrict to the matter vacuum, with the two shown to be unitarily equivalent for finite gauge groups.
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Hadronic scattering in (1+1)D SU(2) lattice gauge theory from tensor networks
First tensor-network simulation of real-time hadronic scattering in (1+1)D SU(2) lattice gauge theory reveals entanglement and spatial delocalization in the baryon-number-one sector at strong coupling.
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Toward Hamiltonian simulations of Maxwell-Chern-Simons theory: constant modes and gauge field truncation
A lattice discretization of constant modes in 2+1D Maxwell-Chern-Simons theory on a torus maps to a generalized Harper-Hofstadter model, reproducing continuum topological degeneracy under specific commensurability conditions with truncation convergence analyzed.
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Exponentially improved quantum simulation of scalar QFT
Diagonalizing field operators before Pauli-string decomposition exponentially cuts circuit depth and Trotter errors in 2+1D scalar QFT simulations, with faster local-truncation convergence for Lorentzian energy-energy correlators than the Jordan-Lee-Preskill amplitude-basis method.
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Tightening energy-based boson truncation bound using Monte Carlo-assisted methods
New analytic and Monte Carlo-assisted method tightens energy-based boson truncation bounds, reducing volume dependence in (1+1)D scalar and (2+1)D U(1) gauge theories.
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Quantum Simulation of Nucleon-Antinucleon Interaction in Large-$N$ QCD$_2$ on an IBM Quantum Nighthawk Processor
Quantum simulation on IBM Nighthawk extracts attractive kink-antikink potential in large-N QCD2 mapped to XXZ chain, agreeing with exact diagonalization benchmarks.
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Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs
Quantum advantage in hadronic tomography should be evaluated selectively for CFFs, GPDs, TMDs, and GTMDs because their light-front and real-time correlation functions create ill-posed inverse problems that quantum algorithms may address at algorithmic, computational, and inference levels.
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Quantum Simulation of Gauge Theories for Particle and Nuclear Physics
The talk summarizes the quantum simulation program for lattice gauge theories, covering target problems in dense matter, algorithmic strategies, recent progress, and remaining challenges.