Toward Quantum-Centric Simulations of Extended Molecules: Sample-Based Quantum Diagonalization Enhanced with Density Matrix Embedding Theory.

Computing ground-state properties of molecules is a promising application for quantum computers operating in concert with classical high-performance computing resources. Quantum embedding methods are a family of algorithms particularly suited to these computational platforms: they combine high-level calculations on active regions of a molecule with low-level calculations on the surrounding environment, thereby avoiding expensive high-level full-molecule calculations and allowing the distribution of computational cost across multiple and heterogeneous computing units. Here, we present the first density matrix embedding theory (DMET) simulations performed in combination with a sample-based quantum diagonalization (SQD) method.

Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer.

A universal quantum computer can simulate diverse quantum systems, with electronic structure for chemistry offering challenging problems for practical use cases around the hundred-qubit mark. Although current quantum processors have reached this size, deep circuits and a large number of measurements lead to prohibitive runtimes for quantum computers in isolation. Here, we demonstrate the use of classical distributed computing to offload all but an intrinsically quantum component of a workflow for electronic structure simulations.

Quantum-Centric Computational Study of Methylene Singlet and Triplet States.

This study involves quantum simulations of the dissociation of the ground-state triplet and first excited singlet states of the CH molecule (methylene), which are relevant for interstellar and combustion chemistry. These were modeled as (6e, 23o) systems using 52 qubits on a quantum processor by applying the sample-based quantum diagonalization (SQD) method within a quantum-centric supercomputing framework. We evaluated the ability of SQD to provide accurate results of the singlet-triplet gap in comparison to selected configuration interaction (SCI) calculations and experimental values.

Accurate quantum-centric simulations of supramolecular interactions.

We present the first quantum-centric simulations of noncovalent interactions using a supramolecular approach. We simulate the potential energy surfaces (PES) of the water and methane dimers, featuring hydrophilic and hydrophobic interactions, respectively, with a sample-based quantum diagonalization (SQD) approach. Our simulations on quantum processors, using 27- and 36-qubit circuits, are in remarkable agreement with classical methods, deviating from complete active space configuration interaction (CASCI) and coupled-cluster singles, doubles, and perturbative triples (CCSD(T)) within 1 kcal/mol in the equilibrium regions of the PES.

Mott Transition and Volume Law Entanglement with Neural Quantum States.

The interplay between delocalization and repulsive interactions can cause electronic systems to undergo a Mott transition between a metal and an insulator. Here we use neural network hidden fermion determinantal states (HFDS) to uncover this transition in the disordered, fully connected Hubbard model. While dynamical mean-field theory (DMFT) provides exact solutions to physical observables of the model in the thermodynamic limit, our method allows us to directly access the wave function for finite system sizes well beyond the reach of exact diagonalization.

Variational benchmarks for quantum many-body problems.

The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide an extensive curated dataset of variational calculations of many-body quantum systems, identifying cases where state-of-the-art numerical approaches show limited accuracy and future algorithms or computational platforms, such as quantum computing, could provide improved accuracy.

Fermionic wave functions from neural-network constrained hidden states.

We introduce a systematically improvable family of variational wave functions for the simulation of strongly correlated fermionic systems. This family consists of Slater determinants in an augmented Hilbert space involving "hidden" additional fermionic degrees of freedom. These determinants are projected onto the physical Hilbert space through a constraint that is optimized, together with the single-particle orbitals, using a neural network parameterization.

Deep Learning the Hohenberg-Kohn Maps of Density Functional Theory.

A striking consequence of the Hohenberg-Kohn theorem of density functional theory is the existence of a bijection between the local density and the ground-state many-body wave function. Here we study the problem of constructing approximations to the Hohenberg-Kohn map using a statistical learning approach. Using supervised deep learning with synthetic data, we show that this map can be accurately constructed for a chain of one-dimensional interacting spinless fermions in different phases of this model including the charge ordered Mott insulator and metallic phases and the critical point separating them.

Field enhancement and spectral features of hexagonal necklaces of silver nanoparticles for enhanced nonlinear optical processes.

The nonlinear properties of hybrid metallic-dielectric systems are attracting great interest due to their potential for the enhancement of frequency conversion processes at nanoscale dimensions. In this work, we theoretically and experimentally address the correlation between the near field distribution of hexagonal plasmonic necklaces of silver nanoparticles formed on the surface of a LiNbO crystal and the second harmonic generation (SHG) produced by this nonlinear crystal in the vicinities of the necklaces. The spectral response of the hexagonal necklaces does not depend on the polarization direction and is characterized by two main modes, the absorptive high-energy mode located in the UV spectral region and the lower energy mode, which is strongly radiant and extends from the visible to the near infrared region.

Information processing in patterned magnetic nanostructures with edge spin waves.

Low dissipation data processing with spins is one of the promising directions for future information and communication technologies. Despite a significant progress, the available magnonic devices are not broadband yet and have restricted capabilities to redirect spin waves. Here we propose a breakthrough approach to spin wave manipulation in patterned magnetic nanostructures with unmatched characteristics, which exploits a spin wave analogue to edge waves propagating along a water-wall boundary.