In the first chapter of this thesis, a background of fundamental quantum chemistry concepts is provided. Chapter two contains an analysis of the performance and energy efficiency of various modern computer processor architectures while performing computational chemistry\ncalculations. In chapter three, the processor architectural study is expanded to include parallel computational chemistry algorithms executed across multiple-node computer clusters. Chapter four describes a novel computational implementation of the fundamental Hartree-Fock method which significantly reduces computer memory requirements. In chapter five, a case study of quantum chemistry two-electron integral code interoperability is described. The final chapters of this work discuss applications of quantum chemistry. In chapter six, an investigation of the esterification of acetic acid on acid-functionalized silica is presented. In chapter seven, the application of ab initio molecular dynamics to study the photoisomerization and photocyclization of stilbene is discussed. Final concluding remarks are noted in chapter eight.
The two-electron repulsion integral (ERI) is a fundamental component in quantum chemistry, involving the calculation of integrals over four basis functions. Quantum chemistry computations require O(M4) 4 ERI calculations for M basis functions, making ERI evaluations a significant bottleneck in these computations. In this paper, we propose an efficient GPU-based parallelization of the McMurchie-Davidson algorithm, which recursively computes ERI. Our method focuses on optimizing the computations by leveraging the shell parameter, which characterizes the shape of the basis functions. The results demonstrate that the shell-based optimization achieves a performance improvement of up to 15% compared to the nonoptimized approach.
Fragment molecular orbital (FMO) quantum chemistry offers a natural route to parallel quantum computing: large molecular calculations can be decomposed into smaller quantum subproblems that can, in principle, run concurrently. Their practical benefit on real quantum hardware, however, has remained unclear. We demonstrate parallel FMO calculations on three room-temperature diamond-based 'Quoll' quantum computers installed on-site at the Oak Ridge Leadership Computing Facility. Helium clusters (He
n,
n=2,4,8,10,14) served as a testbed for total energy calculations using the Quantum Wave Function Sampler (QWFS), a selected configuration interaction (SCI) approach. We compare two execution modes: synchronous shot-parallel execution, in which quantum processors jointly sample QWFS circuits and combine their SPAM-corrected counts into a single probability distribution; and asynchronous fragment-parallel execution, in which processors independently evaluate FMO fragments using device-specific optimized circuits. Both modes increase quantum sampling throughput without systematic loss of accuracy. The three-QPU shot-parallel workflow reaches a parallel efficiency of
93.60%, while the fragment-parallel workflow achieves a measured efficiency of
75.01%, increasing to
95.17% when excluding the non-useful idle time associated with non-interruptible submitted jobs. In both cases, the assembled FMO-QWFS energies remain chemically accurate compared to the classical ideal. These results provide a proof-of-concept demonstration that parallel quantum execution can improve the practical throughput of quantum chemistry workflows on prototype hardware, motivating the development of future HPC environments with many fault-tolerant quantum processors operating as a distributed resource for molecular and materials simulation.