  Accurate and scalable methods for computational quantum chemistry can
accelerate research and development in many fields, ranging from drug discovery
to advanced material design. Solving the electronic Schrodinger equation is the
core problem of computational chemistry. However, the combinatorial complexity
of this problem makes it intractable to find exact solutions, except for very
small systems. The idea of quantum computing originated from this computational
challenge in simulating quantum-mechanics. We propose an end-to-end quantum
chemistry pipeline based on the variational quantum eigensolver (VQE) algorithm
and integrated with both HPC-based simulators and a trapped-ion quantum
computer. Our platform orchestrates hundreds of simulation jobs on compute
resources to efficiently complete a set of ab initio chemistry experiments with
a wide range of parameterization. Per- and poly-fluoroalkyl substances (PFAS)
are a large family of human-made chemicals that pose a major environmental and
health issue globally. Our simulations includes breaking a Carbon-Fluorine bond
in trifluoroacetic acid (TFA), a common PFAS chemical. This is a common pathway
towards destruction and removal of PFAS. Molecules are modeled on both a
quantum simulator and a trapped-ion quantum computer, specifically IonQ Aria.
Using basic error mitigation techniques, the 11-qubit TFA model (56 entangling
gates) on IonQ Aria yields near-quantitative results with milli-Hartree
accuracy. Our novel results show the current state and future projections for
quantum computing in solving the electronic structure problem, push the
boundaries for the VQE algorithm and quantum computers, and facilitates
development of quantum chemistry workflows.
