arXiv subjects

Roberto Di Remigio Eikås

Publications and source records attributed to Roberto Di Remigio Eikås.

8 recordsLinked to original sources

arXiv2026

Learned Adaptive Multiresolution Diffusion Imaging

Adaptive multiresolution methods reduce representation cost by concentrating fine-scale degrees of freedom where needed, but their tree updates are usually governed by fixed local criteria. We introduce Learned Adaptive Multiresolution Diffusion Imaging (Learned AMDI), which preserves the AMDI fixed-tree propagator and hierarchy constraints while replacing the post-propagation selector with a shared local policy trained by proximal policy optimization. Regression tests reproduce deterministic AMDI trajectories to machine precision when identical trees are used. In the Haar implementation studied here, the deterministic one-step selector accepts no refinements in 54 decisions. Across nine held-out cases, Learned AMDI executes 393 refinements and reduces the mean terminal reference discrepancy from $0.17496$ to $0.13657$, while occupancy rises from $0.13737$ to $0.26660$. Step-resolved diagnostics reveal occasional small adaptation-energy increases; fixed-tree energy stability therefore does not guarantee monotonicity of the learned outer iteration. At comparable occupancy, a validation-tuned observed-detail threshold reaches a discrepancy of $0.13792$ with slightly better RMSE and SSIM, placing both methods on essentially the same accuracy--occupancy tradeoff. A decision-1-only control reaches $0.13742$, indicating that most of the improvement on this static benchmark arises from the initial allocation. The shared actor transfers without retraining to $64\times64$ and $128\times128$ images, improving reference discrepancy, RMSE, and SSIM relative to deterministic AMDI, while the frozen threshold rule remains competitive. Learned AMDI thus provides a hierarchy-constrained, resolution-transferable mechanism for adaptive allocation and clarifies the contribution of sequential decisions.

math.NA↗

arXiv2026

Adaptive Multiresolution Diffusion Operators: A Variational Theory on Evolving Multiresolution Spaces

We develop a variational framework for state-dependent diffusion on adaptive multiresolution representations in which the diffusion operator is generated by the adaptive state itself. The state consists of an admissible multiresolution tree, its active approximation space and basis, and the corresponding coefficient representation. It determines a symmetric nonnegative interaction form and an associated positive semidefinite intrinsic diffusion operator. In contrast to classical adaptive wavelet methods, where a prescribed operator is represented on an evolving approximation space, refinement and coarsening here modify simultaneously the representation, interaction graph, and operator. Because the adaptive hierarchy evolves through discrete topological changes, the coupled dynamics are formulated through a time-discrete variational principle rather than a differential evolution on a fixed space. We establish existence of the discrete updates, a discrete energy inequality, the coefficient-space null mode, and contractivity for frozen adaptive states. For regularized inverse problems, the construction yields Adaptive Multiresolution Diffusion Imaging (AMDI), combining data fidelity, intrinsic diffusion, coefficient sparsity, and tree complexity in a state-dependent energy. Numerical experiments verify the assembled operator identities, examine refinement-commutator decay, and confirm discrete energy dissipation. Adaptive Haar and higher-order multiwavelet calculations demonstrate localization of resolution on heterogeneous data. In denoising, AMDI retains high structural reconstruction quality with less than 10\% of the full active representation, with stable behavior across held-out noise realizations.

math.NA↗

arXiv2026

Four-component relativistic calculations in a multiwavelet basis with improved convergence

We revive an approach to solve the Dirac equation originally proposed by Kutzelnigg which makes use of the squared Dirac operator $\hat{\mathfrak{D}}^{2}$. This approach holds the promise to avoid the negative energy solution because the negative energy spectrum is now ``folded" on the positive energy side and at the same time provides a convex equation, which is amenable to a minimization process and increased precision in the final result. The $\hat{\mathfrak{D}}^{2}$ yields an equation similar to the non-relativistic one, yet in a four-component framework, where Multiwavelet tools and algorithms developed for the non-relativistic case can be employed with minor modifications. On the other hand, the use of Multiwavelets is here essential to achieve the full potential of the approach. We implemented and validated this approach for one- and two-electron systems with increasing nuclear charge. Numerical tests were performed to gauge the actual precision of the approach with respect to either analytical reference values when possible or numerical results obtained with the \textit{GRASP} code otherwise.

physics.chem-ph↗

arXiv2024

Quantum-centric strong and dynamical electron correlation: A resource-efficient second-order $N$-electron valence perturbation theory formulation for near-term quantum devices

We present a measurement-cost efficient implementation of Strongly-Contracted $N$-Electron Valence Perturbation Theory (SC-NEVPT2) for use on near-term quantum devices. At the heart of our algorithm we exploit the properties of adaptive Informationally Complete positive operator valued measures (IC-POVMs) to recycle the measurement outcomes from a ground state energy estimation on a quantum device to reconstruct the matrix elements of the three- and four-body reduced density matrices for use in a subsequent CPU-driven NEVPT2 calculation. The proposed scheme is capable of producing results in good agreement with corresponding conventional NEVPT2 simulations, while significantly reducing the cost of quantum measurements and allowing for embarrassingly parallel estimations of higher-order RDMs in classical post-processing. Our scheme shows favourable scaling of the total number of shots with respect to system size. This paves the way for routine inclusion of dynamic electron correlation effects in hybrid quantum-classical computing pipelines.

quant-ph↗

arXiv2024

VAMPyR -- A High-Level Python Library for Mathematical Operations in a Multiwavelets Representation

Wavelets and Multiwavelets have lately been adopted in Quantum Chemistry to overcome challenges presented by the two main families of basis sets: Gaussian atomic orbitals and plane waves. In addition to their numerical advantages (high precision, locality, fast algorithms for operator application, linear scaling with respect to system size, to mention a few) they provide a framework which narrows the gap between the theoretical formalism of the fundamental equations and the practical implementation in a working code. This realization led us to the development of the Python library called VAMPyR, (Very Accurate Multiresolution Python Routines). VAMPyR encodes the binding to a C++ library for Multiwavelet calculations (algebra, integral and differential operator application) and exposes the required functionality to write simple Python code to solve among others, the Hartree--Fock equations, the generalized Poisson Equation, the Dirac equation and the time-dependent Schrödinger equation up to any predefined precision. In this contribution we will outline the main features of Multiresolution Analysis using multiwavelets and we will describe the design of the code. A few illustrative examples will show the code capabilities and its interoperability with other software platforms.

physics.chem-ph↗

arXiv2023

Full Breit Hamiltonian in the Multiwavelets Framework

New techniques in core-electron spectroscopy are necessary to resolve the structures of oxides of $f$-elements and other strongly correlated materials that are present only as powders and not as single crystals. Thus, accurate quantum chemical methods need to be developed to calculate core spectroscopic properties in such materials. In this contribution, we present an important development in this direction, extending our fully adaptive real-space multiwavelet basis framework to tackle the 4-component Dirac-Coulomb-Breit Hamiltonian. We show that Multiwavelets are able to reproduce one-dimensional grid-based approaches. They are however a fully three-dimensional approach which can later on be extended to molecules and materials. Our Multiwavelet implementation attained precise results irrespective of the chosen nuclear model, provided that the error threshold is tight enough and the chosen polynomial basis is sufficiently large. Furthermore, our results confirmed that in two-electron species, the magnetic and Gauge contributions from $s$-orbitals are identical in magnitude and can account for the experimental evidence from $K$ and $L$ edges.

physics.chem-ph↗

arXiv2023

Cavity-free continuum solvation: implementation and parametrization in a multiwavelet framework

We present a multiwavelet-based implementation of a quantum/classical polarizable continuum model. The solvent model uses a diffuse solute-solvent boundary and a position-dependent permittivity, lifting the sharp-boundary assumption underlying many existing continuum solvation models. We are able to include both surface and volume polarization effects in the quantum/classical coupling, with guaranteed precision, due to the adaptive refinement strategies of our multiwavelet implementation. The model can account for complex solvent environments and does not need a posteriori corrections for volume polarization effects. We validate our results against a sharp-boundary continuum model and find very good correlation of the polarization energies computed for the Minnesota solvation database.

physics.chem-ph↗

arXiv2022

The MRChem multiresolution analysis code for molecular electronic structure calculations: performance and scaling properties

MRChem is a code for molecular electronic structure calculations, based on a multiwavelet adaptive basis representation. We provide a description of our implementation strategy and several benchmark calculations. Systems comprising more than a thousand orbitals are investigated at the HF level of theory, with an emphasis on scaling properties. With our design, terms which formally scale quadratically with the system size, in effect have a better scaling because of the implicit screening introduced by the inherent adaptivity of the method: all operations are performed to the requested precision, which serves the dual purpose of minimizing the computational cost and controlling the final error precisely. Comparisons with traditional Gaussian-type orbitals based software, show that MRChem can be competitive with respect to performance.

physics.chem-ph↗