Quantum simulation on trapped ions shows that a plaquette term in a 2+1D U(1) gauge theory enables string propagation in the plane and extended matter creation, realizing genuine two-dimensional dynamics.
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A trapped-ion quantum computer simulates 2+1D Z2 lattice gauge theory dynamics, revealing glueball excitations and multi-order string breaking.
Quantum hardware simulation of SU(2) lattice gauge thermalization matches classical extrapolations up to 101 plaquettes after error mitigation, establishing feasibility for chaotic quantum field systems.
A method using dressed creation operators from MLWFs enables selective preparation and detection of quasiparticles in lattice theories, tested via MPS on hardcore QCD ladders to separate known excitations from resonances.
A 4-qubit QCNN classifies entanglement thresholds from fermion density profiles in the Thirring model more effectively than comparable classical CNNs.
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.
citing papers explorer
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Observation of glueball excitations and string breaking in a $2+1$D $\mathbb{Z}_2$ lattice gauge theory on a trapped-ion quantum computer
A trapped-ion quantum computer simulates 2+1D Z2 lattice gauge theory dynamics, revealing glueball excitations and multi-order string breaking.
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Observation of genuine $2+1$D string dynamics in a U$(1)$ lattice gauge theory with a tunable plaquette term on a trapped-ion quantum computer
Quantum simulation on trapped ions shows that a plaquette term in a 2+1D U(1) gauge theory enables string propagation in the plane and extended matter creation, realizing genuine two-dimensional dynamics.
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Thermalization of SU(2) Lattice Gauge Fields on Quantum Computers
Quantum hardware simulation of SU(2) lattice gauge thermalization matches classical extrapolations up to 101 plaquettes after error mitigation, establishing feasibility for chaotic quantum field systems.
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Preparation and detection of quasiparticles for quantum simulations of scattering
A method using dressed creation operators from MLWFs enables selective preparation and detection of quasiparticles in lattice theories, tested via MPS on hardcore QCD ladders to separate known excitations from resonances.
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Quantum Machine Learning for particle scattering entanglement classification
A 4-qubit QCNN classifies entanglement thresholds from fermion density profiles in the Thirring model more effectively than comparable classical CNNs.
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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.
- Tightening energy-based boson truncation bound using Monte Carlo-assisted methods