DREAM enables exact-gradient Bayesian calibration of nuclear models via offline SVD emulation of parameter-dependent operators, demonstrated by rapid HMC convergence on an 18-parameter CDCC analysis of d+58Ni scattering.
Eigenvector continuation with subspace learning
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
A common challenge faced in quantum physics is finding the extremal eigenvalues and eigenvectors of a Hamiltonian matrix in a vector space so large that linear algebra operations on general vectors are not possible. There are numerous efficient methods developed for this task, but they generally fail when some control parameter in the Hamiltonian matrix exceeds some threshold value. In this work we present a new technique called eigenvector continuation that can extend the reach of these methods. The key insight is that while an eigenvector resides in a linear space with enormous dimensions, the eigenvector trajectory generated by smooth changes of the Hamiltonian matrix is well approximated by a very low-dimensional manifold. We prove this statement using analytic function theory and propose an algorithm to solve for the extremal eigenvectors. We benchmark the method using several examples from quantum many-body theory.
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A structure-preserving low-rank factorization of 2RDMs achieves linear rank scaling with system size and ~99% compression while retaining chemical accuracy for correlated states.
Perturbative N3LO calculations in chiral EFT with RG-guided power counting yield robust predictions for light nuclei energies when calibrated on the tritium binding energy.
TETRIS-ADAPT-VQE achieves fidelities above 99.3% for SYK (N=20) and 99.9998% for SK (L=18) but requires large resources for SYK models.
The review describes how SSRPA fixes SRPA pathologies in EDF theory, presents applications to charge-conserving and charge-exchange excitations with experimental comparisons, and discusses impacts on the nuclear equation of state.
citing papers explorer
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High-Dimensional Bayesian Calibration of Expensive Nuclear Models with Differentiable Emulation
DREAM enables exact-gradient Bayesian calibration of nuclear models via offline SVD emulation of parameter-dependent operators, demonstrated by rapid HMC convergence on an 18-parameter CDCC analysis of d+58Ni scattering.
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Low-rank compression of two-electron reduced density matrices
A structure-preserving low-rank factorization of 2RDMs achieves linear rank scaling with system size and ~99% compression while retaining chemical accuracy for correlated states.
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Perturbative calculations of light nuclei up to N$^3$LO in chiral effective field theory
Perturbative N3LO calculations in chiral EFT with RG-guided power counting yield robust predictions for light nuclei energies when calibrated on the tritium binding energy.
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Ground state preparation of random all-to-all Hamiltonians using ADAPT-VQE
TETRIS-ADAPT-VQE achieves fidelities above 99.3% for SYK (N=20) and 99.9998% for SK (L=18) but requires large resources for SYK models.
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Recent applications of the subtracted second RPA method
The review describes how SSRPA fixes SRPA pathologies in EDF theory, presents applications to charge-conserving and charge-exchange excitations with experimental comparisons, and discusses impacts on the nuclear equation of state.