Matrix product states allow amplitude encoding of Slater-type orbitals with constant bond dimension in one dimension and saturating entanglement in three dimensions, supporting low-error integral evaluation on quantum processors.
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A digital quantum processor simulates the 1D Fermi-Hubbard model on up to 120 qubits, observing spin-charge separation and achieving quantitative agreement with TDVP while running up to 3000 times faster in wall-clock time for long evolutions.
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.
Structure-aware VQE ansatze for long-range Ising models cut required circuit layers by 2.5x to 3.8x in non-local regimes while two-qubit gate counts scale quadratically with system size, consistent with the number of Hamiltonian terms.
A Slater-determinant-to-qubit mapping enables low-depth VQE circuits for nuclear shell model calculations on NISQ hardware, achieving less than 4% deviation from classical predictions after zero-noise extrapolation for nuclei including lithium isotopes and 210Pb.
citing papers explorer
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Amplitude Encoding of Slater-Type Orbitals via Matrix Product States: Efficient State Preparation and Integral Evaluation on Quantum Hardware
Matrix product states allow amplitude encoding of Slater-type orbitals with constant bond dimension in one dimension and saturating entanglement in three dimensions, supporting low-error integral evaluation on quantum processors.
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Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor
A digital quantum processor simulates the 1D Fermi-Hubbard model on up to 120 qubits, observing spin-charge separation and achieving quantitative agreement with TDVP while running up to 3000 times faster in wall-clock time for long evolutions.
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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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Scaling of Quantum Resources for Simulating a Long-Range System
Structure-aware VQE ansatze for long-range Ising models cut required circuit layers by 2.5x to 3.8x in non-local regimes while two-qubit gate counts scale quadratically with system size, consistent with the number of Hamiltonian terms.
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A low-circuit-depth quantum computing approach to the nuclear shell model
A Slater-determinant-to-qubit mapping enables low-depth VQE circuits for nuclear shell model calculations on NISQ hardware, achieving less than 4% deviation from classical predictions after zero-noise extrapolation for nuclei including lithium isotopes and 210Pb.