arXiv subjects

Claudia Filippi

Publications and source records attributed to Claudia Filippi.

At least 19 recordsLinked to original sources

arXiv2026

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test

We introduce quantum Monte Carlo (QMC)-trained multi-state machine-learned (ML) force fields for nonadiabatic excited-state dynamics, targeting photochemical processes in which the electronic character changes along the reaction path and a consistent correlated description is required. In this framework, variational Monte Carlo wave functions combine compact selected configuration-interaction expansions with a Jastrow factor that explicitly accounts for dynamical correlation, while neural networks convert the stochastic QMC data into smooth potential energy surfaces for large surface-hopping ensembles. We apply this approach to azomethane, a demanding test case involving torsional relaxation through conical-intersection regions and C--N bond dissociation on the hot ground state. Benchmark calculations support the accuracy of the QMC reference data and show robust force convergence across isomerization and dissociation geometries. The QMC-trained dynamics preserves the expected photoisomerization mechanism, strongly reduces the excessive C--N breaking obtained with complete active space self-consistent field, and predicts a small but non-negligible prompt dissociation component after internal conversion, with a timescale consistent with femtosecond-resolved mass-spectrometry experiments. These results establish QMC-ML as a practical route to nonadiabatic photochemical dynamics with accurate wave-function reference data.

physics.chem-ph↗

arXiv2025

QMCkl: A Kernel Library for Quantum Monte Carlo Applications

Quantum Monte Carlo (QMC) methods deliver highly accurate electronic structure calculations but are computationally intensive. The quantum Monte Carlo kernel library (QMCkl) provides a modular, portable collection of high-performance kernels implementing the core building blocks of QMC calculations. It offers a C-compatible API, supports the TREXIO standard for input, and covers essential QMC kernels including atomic and molecular orbitals, cusp corrections, Jastrow factor, and the necessary derivatives also to perform variational and structural optimization. QMCkl separates algorithmic development from hardware-specific tuning by combining human-readable reference implementations with performance-optimized kernels that produce identical numerical results. The library enables consistent, efficient, and reproducible simulations across different QMC codes and architectures, and achieves substantial speedups in the evaluation of the energy and its derivatives. Beyond QMC, QMCkl can accelerate deterministic quantum chemistry workflows and visualization tools, promoting cross-code interoperability and simplifying high-performance scientific software development.

physics.chem-ph↗

arXiv2025

Experimental demonstration of boson sampling as a hardware accelerator for monte carlo integration

We present an experimental demonstration of boson sampling as a hardware accelerator for Monte Carlo integration. Our approach leverages importance sampling to factorize an integrand into a distribution that can be sampled using quantum hardware and a function that can be evaluated classically, enabling hybrid quantum-classical computation. We argue that for certain classes of integrals, this method offers a quantum advantage by efficiently sampling from probability distributions that are hard to simulate classically. We also identify structural criteria that must be satisfied to preserve computational hardness, notably the sensitivity of the classical post-processing function to high-order quantum correlations. To validate our protocol, we implement a proof-of-principle experiment on a programmable photonic platform to compute the first-order energy correction of a three-boson system in a harmonic trap under an Efimov-inspired three-body perturbation. The experimental results are consistent with theoretical predictions and numerical simulations, with deviations explained by photon distinguishability, discretization, and unitary imperfections. Additionally, we provide an error budget quantifying the impact of these same sources of noise. Our work establishes a concrete use case for near-term photonic quantum devices and highlights a viable path toward practical quantum advantage in scientific computing.

quant-ph↗

arXiv2025

Optimizing excited states in quantum Monte Carlo: A reassessment of double excitations

Quantum Monte Carlo (QMC) methods have proven to be highly accurate for computing excited states, but the choice of optimization strategies for multiple states remains an active topic of investigation. In this work, we revisit the calculation of double excitation energies in nitroxyl, glyoxal, tetrazine, and cyclopentadienone, exploring different objective functionals and their impact on the accuracy and robustness of QMC. A previous study for these systems employed a penalty functional to enforce orthogonality among the states, but the chosen prefactors did not strictly ensure convergence to the target states. Here, we confirm the reliability of previous results by comparing excitation energies obtained with different functionals and analyzing their consistency. Additionally, we investigate the performance of different functionals when starting from a pre-collapsed excited state, providing insight into their ability to recover the target wave functions.

physics.comp-ph↗

arXiv2025

Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"{o}dinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to assess the reproducibility of the method, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes-highlighting the maturity and robustness of the method as a tool for open and reliable computational science.

physics.comp-ph↗

arXiv2024

Shifting sands of hardware and software in exascale quantum mechanical simulations

The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. Although the transition from petaflops to exascale computing has been marked by a steady increase in computational power, it is accompanied by a shift towards heterogeneous architectures, with graphical processing units (GPUs) in particular gaining a dominant role. The exascale era, therefore, demands a fundamental shift in software development strategies. This Perspective examines the changing landscape of hardware and software for exascale computing, highlighting the limitations of traditional algorithms and software implementations in light of the increasing use of heterogeneous architectures in high-end systems. We discuss the challenges of adapting quantum chemistry software to these new architectures, including the fragmentation of the software stack, the need for more efficient algorithms (including reduced precision versions) tailored for GPUs, and the importance of developing standardized libraries and programming models.

physics.comp-ph↗

arXiv2024

Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark

Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by using either multi-determinant Jastrow-Slater wave functions in variational Monte Carlo or just a single determinant in diffusion Monte Carlo. The excellent performance of our protocols is assessed against high-level coupled cluster calculations on a diverse set of representative configurations of the system. Finally, we train machine-learning force fields on the QMC forces and compare them to models trained on coupled cluster reference data, showing that a force field based on the diffusion Monte Carlo forces with a single determinant can faithfully reproduce coupled cluster power spectra in molecular dynamics simulations.

physics.chem-ph↗

arXiv2023

TREXIO: A File Format and Library for Quantum Chemistry

TREXIO is an open-source file format and library developed for the storage and manipulation of data produced by quantum chemistry calculations. It is designed with the goal of providing a reliable and efficient method of storing and exchanging wave function parameters and matrix elements, making it an important tool for researchers in the field of quantum chemistry. In this work, we present an overview of the TREXIO file format and library. The library consists of a front-end implemented in the C programming language and two different back-ends: a text back-end and a binary back-end utilizing the HDF5 library which enables fast read and write operations. It is compatible with a variety of platforms and has interfaces for the Fortran, Python, and OCaml programming languages. In addition, a suite of tools has been developed to facilitate the use of the TREXIO format and library, including converters for popular quantum chemistry codes and utilities for validating and manipulating data stored in TREXIO files. The simplicity, versatility, and ease of use of TREXIO make it a valuable resource for researchers working with quantum chemistry data.

physics.chem-ph↗

arXiv2022

Double excitation energies from quantum Monte Carlo using state-specific energy optimization

We show that recently developed quantum Monte Carlo methods, which provide accurate vertical transition energies for single excitations, also successfully treat double excitations. We study the double excitations in medium-sized molecules, some of which are challenging for high level coupled-cluster calculations to model accurately. Our fixed-node diffusion Monte Carlo excitation energies are in very good agreement with reliable benchmarks, when available, and provide accurate predictions for excitation energies of difficult systems where reference values are lacking.

physics.comp-ph↗

arXiv2021

Reference excitation energies of increasingly large molecules: a QMC study of cyanine dyes

We revisit here the lowest vertical excitations of cyanine dyes using quantum Monte Carlo and leverage on recent developments to systematically improve on previous results. In particular, we employ a protocol for the construction of compact and accurate multi-determinant Jastrow-Slater wave functions for multiple states, which we have recently validated on the excited-state properties of several small prototypical molecules. Here, we obtain quantum Monte Carlo excitation energies in excellent agreement with high-level coupled cluster for all the cyanines where the coupled cluster method is applicable. Furthermore, we push our protocol to longer chains, demonstrating that quantum Monte Carlo is a viable methodology to establish reference data at system sizes which are hard to reach with other high-end approaches of similar accuracy. Finally, we determine which ingredients are key to an accurate treatment of these challenging systems and rationalize why a description of the excitation based on only active $π$ orbitals lacks the desired accuracy for the shorter chains.

physics.chem-ph↗

arXiv2021

Energy derivatives in real-space diffusion Monte Carlo

We present unbiased, finite--variance estimators of energy derivatives for real--space diffusion Monte Carlo calculations within the fixed--node approximation. The derivative $d_λE$ is fully consistent with the dependence $E(λ)$ of the energy computed with the same time step. We address the issue of the divergent variance of derivatives related to variations of the nodes of the wave function, both by using a regularization for wave function parameter gradients recently proposed in variational Monte Carlo, and by introducing a regularization based on a coordinate transformation. The essence of the divergent variance problem is distilled into a particle-in-a-box toy model, where we demonstrate the algorithm.

cond-mat.mtrl-sci↗

arXiv2021

Tailoring CIPSI expansions for QMC calculations of electronic excitations: the case study of thiophene

The perturbatively selected configuration interaction scheme (CIPSI) is particularly effective in constructing determinantal expansions for quantum Monte Carlo (QMC) simulations with Jastrow-Slater wave functions: fast and smooth convergence of ground-state properties, as well as balanced descriptions of ground- and excited-states of different symmetries have been reported. In particular, accurate excitation energies have been obtained by the pivotal requirement of using CIPSI expansions with similar second-order perturbation corrections for each state, that is, similar estimated errors with respect to the full configuration interaction limit. Here we elaborate on the CIPSI selection criterion for excited states of the same symmetry as the ground state, generating expansions from a common orbital set. Using these expansions in QMC as determinantal components of Jastrow-Slater wave functions, we compute the lowest, bright excited state of thiophene, which is challenging due to its significant multireference character. The resulting vertical excitation energies are within 0.05~eV of the best theoretical estimates, already with expansions of only a few thousand determinants. Furthermore, we relax the ground- and excited-state structures following the corresponding root in variational Monte Carlo and obtain bond lengths which are accurate to better than 0.01~Å. Therefore, while the full treatment at the CIPSI level of this system would be quite demanding, in QMC we can compute high-quality excitation energies and excited-state structural parameters building on affordable CIPSI expansions with relatively few, well chosen determinants.

physics.class-ph↗

arXiv2020

Variational principles in quantum Monte Carlo: the troubled story of variance minimization

We investigate the use of different variational principles in quantum Monte Carlo, namely energy and variance minimization, prompted by the interest in the robust and accurate estimate of electronic excited states. For two prototypical, challenging molecules, we readily reach the accuracy of the best available reference excitation energies using energy minimization in a state-specific or state-average fashion for states of different or equal symmetry, respectively. On the other hand, in variance minimization, where the use of suitable functionals is expected to target specific states regardless of the symmetry, we encounter severe problems for a variety of wave functions: as the variance converges, the energy drifts away from that of the selected state. This unexpected behavior is sometimes observed even when the target is the ground state, and generally prevents the robust estimate of total and excitation energies. We analyze this problem using a very simple wave function and infer that the optimization finds little or no barrier to escape from a local minimum or local plateau, eventually converging to the unique lowest-variance state instead of the target state. While the loss of the state of interest can be delayed and possibly avoided by reducing the statistical error of the gradient, for the full optimization of realistic wave functions, variance minimization with current functionals appears to be an impractical route.

physics.chem-ph↗

arXiv2020

Excited-state calculations with quantum Monte Carlo

Quantum Monte Carlo methods are first-principle approaches that approximately solve the Schrödinger equation stochastically. As compared to traditional quantum chemistry methods, they offer important advantages such as the ability to handle a large variety of many-body wave functions, the favorable scaling with the number of particles, and the intrinsic parallelism of the algorithms which are particularly suitable to modern massively parallel computers. In this chapter, we focus on the two quantum Monte Carlo approaches most widely used for electronic structure problems, namely, the variational and diffusion Monte Carlo methods. We give particular attention to the recent progress in the techniques for the optimization of the wave function, a challenging and important step to achieve accurate results in both the ground and the excited state. We conclude with an overview of the current status of excited-state calculations for molecular systems, demonstrating the potential of quantum Monte Carlo methods in this field of applications.

physics.chem-ph↗

arXiv2019

Excited states with selected CI-QMC: chemically accurate excitation energies and geometries

We employ quantum Monte Carlo to obtain chemically accurate vertical and adiabatic excitation energies, and equilibrium excited-state structures for the small, yet challenging, formaldehyde and thioformaldehyde molecules. A key ingredient is a robust protocol to obtain balanced ground- and excited-state Jastrow-Slater wave functions at a given geometry, and to maintain such a balanced description as we relax the structure in the excited state. We use determinantal components generated via a selected configuration interaction scheme which targets the same second-order perturbation energy correction for all states of interest at different geometries, and we fully optimize all variational parameters in the resultant Jastrow-Slater wave functions. Importantly, the excitation energies as well as the structural parameters in the ground and excited states are converged with very compact wave functions comprising few thousand determinants in a minimally augmented double-$ζ$ basis set. These results are obtained already at the variational Monte Carlo level, the more accurate diffusion Monte Carlo method yielding only a small improvement in the adiabatic excitation energies. We find that matching Jastrow-Slater wave functions with similar variances can yield excitations compatible with our best estimates; however, the variance-matching procedure requires somewhat larger determinantal expansions to achieve the same accuracy, and it is less straightforward to adapt during structural optimization in the excited state.

physics.comp-ph↗

arXiv2018

Perturbatively selected configuration-interaction wave functions for efficient geometry optimization in quantum Monte Carlo

We investigate the performance of a class of compact and systematically improvable Jastrow-Slater wave functions for the efficient and accurate computation of structural properties, where the determinantal component is expanded with a perturbatively selected configuration interaction scheme (CIPSI). We concurrently optimize the molecular ground-state geometry and full wave function -- Jastrow factor, orbitals, and configuration interaction coefficients-- in variational Monte Carlo (VMC) for the prototypical case of 1,3-trans-butadiene, a small yet theoretically challenging $π$-conjugated system. We find that the CIPSI selection outperforms the conventional scheme of correlating orbitals within active spaces chosen by chemical intuition: it gives significantly better variational and diffusion Monte Carlo energies for all but the smallest expansions, and much smoother convergence of the geometry with the number of determinants. In particular, the optimal bond lengths and bond-length alternation of butadiene are converged to better than one mÅ with just a few thousand determinants, to values very close to the corresponding CCSD(T) results. The combination of CIPSI expansion and VMC optimization represents an affordable tool for the determination of accurate ground-state geometries in quantum Monte Carlo.

physics.chem-ph↗

arXiv2017

Optimizing the energy with quantum Monte Carlo: A lower numerical scaling for Jastrow-Slater expansions

We present an improved formalism for quantum Monte Carlo calculations of energy derivatives and properties (e.g. the interatomic forces), with a multideterminant Jastrow-Slater function. As a function of the number $N_e$ of Slater determinants, the numerical scaling of $O(N_e)$ per derivative we have recently reported is here lowered to $O(N_e)$ for the entire set of derivatives. As a function of the number of electrons $N$, the scaling to optimize the wave function and the geometry of a molecular system is lowered to $O(N^3)+O(N N_e)$, the same as computing the energy alone in the sampling process. The scaling is demonstrated on linear polyenes up to C$_{60}$H$_{62}$ and the efficiency of the method is illustrated with the structural optimization of butadiene and octatetraene with Jastrow-Slater wave functions comprising as many as 200000 determinants and 60000 parameters.

physics.chem-ph↗

arXiv2016

Simple formalism for efficient derivatives and multi-determinant expansions in quantum Monte Carlo

We present a simple and general formalism to compute efficiently the derivatives of a multi-determinant Jastrow-Slater wave function, the local energy, the interatomic forces, and similar quantities needed in quantum Monte Carlo. Through a straightforward manipulation of matrices evaluated on the occupied and virtual orbitals, we obtain an efficiency equivalent to algorithmic differentiation in the computation of the interatomic forces and the optimization of the orbital paramaters. Furthermore, for a large multi-determinant expansion, the significant computational gain recently reported for the calculation of the wave function is here improved and extended to all local properties in both all-electron and pseudopotential calculations.

cond-mat.mtrl-sci↗