First NQS variational Monte Carlo calculation of excited states in A=4 nuclei and hypernuclei, reproducing benchmarks and providing the first ab initio M1 transition strength for ^{4}_ΛH consistent with weak-coupling limit at 1.3% suppression.
arXiv:1909.08423
7 Pith papers cite this work, alongside 471 external citations. Polarity classification is still indexing.
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Clifford disentanglers classified by Schmidt spectrum action reduce energy errors at fixed bond dimension in MPS simulations of molecules and improve shallow-circuit VQE calculations.
Projected Inverse Iteration reframes ground-state search for neural quantum states as an eigenvalue problem to deliver rapid, spectral-gap-insensitive convergence while retaining polynomial scaling.
A reorganized Hartree-Fock framework imposes tunable orbital locality by pairing local degrees of freedom with local solution conditions, maintaining efficient SCF optimization and competitive reaction-energy accuracy.
Bargmann zeros of double-well eigenstates condense on the imaginary axis as a signature of the tunneling regime, obtained via variational wavefunctions projected to the Fock basis.
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
citing papers explorer
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Neural-network excited states of $A=4$ nuclei and hypernuclei
First NQS variational Monte Carlo calculation of excited states in A=4 nuclei and hypernuclei, reproducing benchmarks and providing the first ab initio M1 transition strength for ^{4}_ΛH consistent with weak-coupling limit at 1.3% suppression.
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Clifford disentanglers for entanglement reduction in molecular electronic structure simulations
Clifford disentanglers classified by Schmidt spectrum action reduce energy errors at fixed bond dimension in MPS simulations of molecules and improve shallow-circuit VQE calculations.
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Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States
Projected Inverse Iteration reframes ground-state search for neural quantum states as an eigenvalue problem to deliver rapid, spectral-gap-insensitive convergence while retaining polynomial scaling.
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Approximating Hartree-Fock theory via an efficiently local reformulation
A reorganized Hartree-Fock framework imposes tunable orbital locality by pairing local degrees of freedom with local solution conditions, maintaining efficient SCF optimization and competitive reaction-energy accuracy.
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Bargmann Zeros as a Diagnostic of the Tunneling Transition in Double-Well Quantum Systems
Bargmann zeros of double-well eigenstates condense on the imaginary axis as a signature of the tunneling regime, obtained via variational wavefunctions projected to the Fock basis.
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Absorbing Many-Body Correlations into Core-Optimized Orbitals
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
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Neural-Network-Based Variational Method in Nuclear Density Functional Theory: Application to the Extended Thomas-Fermi Model
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.