Introduces the Born-Reciprocal Tensor Network to realize UV/IR mixing as an entanglement bridge in renormalization geometry, with a large-volume limit restoring standard Wilsonian decoupling.
Real-Time Scattering Processes with Continuous-Variable Quantum Computers,
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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 fluctuation-guided adaptive random compiler for Hamiltonian simulation dynamically adjusts term sampling probabilities according to state sensitivity to improve fidelity over fixed randomized methods.
CV-ADAPT-VQE with tailored symmetry-preserving pools achieves significantly shallower circuits than Hamiltonian-based VQE for bosonic lattice models in GPU classical simulations.
citing papers explorer
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When Renormalisation Remembers: UV/IR Mixing as an Entanglement Bridge
Introduces the Born-Reciprocal Tensor Network to realize UV/IR mixing as an entanglement bridge in renormalization geometry, with a large-volume limit restoring standard Wilsonian decoupling.
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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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Fluctuation-guided adaptive random compiler for Hamiltonian simulation
A fluctuation-guided adaptive random compiler for Hamiltonian simulation dynamically adjusts term sampling probabilities according to state sensitivity to improve fidelity over fixed randomized methods.
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Continuous-variable ADAPT-VQE for bosonic lattice models
CV-ADAPT-VQE with tailored symmetry-preserving pools achieves significantly shallower circuits than Hamiltonian-based VQE for bosonic lattice models in GPU classical simulations.