Optimised Projections for the Ab Initio Simulation of Large and Strongly Correlated Systems

Markus Aichhorn

O. Gunnarsson, O. K. Andersen, A. Svane

Lauretta R. Schwarz

This thesis is concerned with the development of a Projector Quantum Monte Carlo method for non-linear wavefunction ansatzes and its application to strongly correlated materials. This new approach is partially inspired by a prior application of the Full Configuration Interaction Quantum Monte Carlo (FCIQMC) method to the three-band ($p-d$) Hubbard model. Through repeated stochastic application of a projector FCIQMC projects out a stochastic description of the Full Configuration Interaction (FCI) ground state wavefunction, a linear combination of Slater determinants spanning the full Hilbert space. The study of the $p-d$ Hubbard model demonstrates that the nature of this FCI expansion is profoundly affected by the choice of single-particle basis. In a counterintuitive manner, the effectiveness of a one-particle basis to produce a sparse, compact and rapidly converging FCI expansion is not necessarily paralleled by its ability to describe the physics of the system within a single determinant. The results suggest that with an appropriate basis, single-reference quantum chemical approaches may be able to describe many-body wavefunctions of strongly correlated materials. Furthermore, this thesis presents a reformulation of the projected imaginary time evolution of FCIQMC as a Lagrangian minimisation. This naturally allows for the optimisation of polynomial complex wavefunction ansatzes with a polynomial rather than exponential scaling with system size. The proposed approach blurs the line between traditional Variational and Projector Quantum Monte Carlo approaches whilst involving developments from the field of deep-learning neural networks which can be expressed as a modification of the projector. The ability of the developed approach to sample and optimise arbitrary non-linear wavefunctions is demonstrated with several classes of Tensor Network States all of which involve controlled approximations but still retain systematic improvability towards exactness. Thus, by applying the method to strongly-correlated Hubbard models, as well as $\textit{ab-initio}$ systems, including a fully periodic $\textit{ab-initio}$ graphene sheet, many-body wavefunctions and their one- and two-body static properties are obtained. The proposed approach can handle and simultaneously optimise large numbers of variational parameters, greatly exceeding those of alternative Variational Monte Carlo approaches.

Antonios M. Alvertis, Abid Khan, Norm M. Tubman

The accurate first-principles description of strongly correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving compressed many-body Hamiltonians that maintain the essential physics of strongly correlated materials. The solution of these material-specific models is still exponentially difficult to generate on classical computers, but quantum algorithms allow for a significant speed-up in obtaining the ground states of these compressed Hamiltonians. Here, we demonstrate that using quantum algorithms to obtain the properties of downfolded Hamiltonians can indeed yield high-fidelity solutions. By combining ab initio downfolding and variational quantum eigensolvers, we correctly predict the antiferromagnetic state of one-dimensional cuprate ${\mathrm{Ca}}_{2}{\mathrm{Cu}\mathrm{O}}_{3}$, the excitonic ground state of monolayer ${\mathrm{W}\mathrm{Te}}_{2}$, and the charge-ordered state of correlated metal ${\mathrm{Sr}\mathrm{VO}}_{3}$. Numerical simulations using a classical tensor network implementation of variational quantum eigensolvers allow us to simulate large models with up to 54 qubits and encompassing up to four bands in the correlated subspace, which is indicative of the complexity that our framework can address. Through these methods we demonstrate the potential of classical preoptimization and downfolding techniques for enabling efficient materials simulation using quantum algorithms.

Benjamin G. Janesko

Entangled atoms, exemplified by the two singlet-coupled atoms in a dissociating nitrogen molecule, are key to the unique properties of qubits, quantum sensors, and other quantum materials. Simulating entangled atoms and strong electron correlation is a prerequisite for modeling quantum materials and remains an outstanding challenge for theory. I introduce a correlated wave function approach capable of simulating strong electron correlation in large active spaces. Projected-interacting full configuration interaction plus density functional theory PiFCI+DFT combines near-exact correlated wave functions of multiple partially interacting model systems, each corrected by a formally exact density functional. This approach can access large active spaces and visualize entanglement and strong correlation while maintaining competitive accuracy for molecular properties. I demonstrate PiFCI+DFT for general main-group chemistry, benchmark dissociation curves, and a model for a large entangled-atom system: the spin-singlet ground state of a square lattice of 100 nitrogen atoms. This system’s 300-electron active space is out of reach for existing ab initio multireference methods. PiFCI+DFT accurately recovers the strongly correlated dissociation limit at a modest computational cost, motivating further development for modeling quantum materials.

Juana Moreno