Presents a tensor network method in Heisenberg picture for computing permanents in Boson Sampling at optimal classical complexity with extensions to imperfections.
Or´ us, Nature Reviews Physics1, 538 (2019)
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A hybrid optimization strategy using classical pre-compilation, iterative extrapolation, and noise-aware quantum refinement achieves orders-of-magnitude gains in fidelity for state preparation in analog simulators with programmable long-range interactions.
A multi-part truncation for lattice QCD with fermions enables explicit Hamiltonians in 1+1D and 2+1D and string-breaking simulations by capping basis states, electric energy, fermions per site, and using large-Nc matrix element scaling.
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Heisenberg picture tensor network formalism for optical circuits
Presents a tensor network method in Heisenberg picture for computing permanents in Boson Sampling at optimal classical complexity with extensions to imperfections.
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Programming long-range interactions in analog quantum simulators
A hybrid optimization strategy using classical pre-compilation, iterative extrapolation, and noise-aware quantum refinement achieves orders-of-magnitude gains in fidelity for state preparation in analog simulators with programmable long-range interactions.
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Large Nc Truncations for SU(Nc) Lattice Yang-Mills Theory with Fermions
A multi-part truncation for lattice QCD with fermions enables explicit Hamiltonians in 1+1D and 2+1D and string-breaking simulations by capping basis states, electric energy, fermions per site, and using large-Nc matrix element scaling.