Despite ubiquitous applications of chemical dynamics simulations, the rendered computational cost grows exponentially with the degrees of freedom of a chemical system, limiting a classical computer to only specific chemical systems. This "curse of dimensionality" is further compounded by the quantum treatment of nuclear degrees of freedom which requires accurate time evolution of nuclear wavefunctions on precisely computed potential energy surfaces (PES). The computation of PES often needs accurate electronic structure calculations to be performed on an exponentially growing set of nuclear configurations. With the advantage of 2n dimensional exponential Hilbert space for n qubit system, quantum computers are seen as having the potential to solve exponential scaling chemical dynamics problems. However, currently available NISQ computers still hinder progress due to their low qubit counts and low-fidelity operations. One way to mitigate this issue is to use hybrid computing by pre-processing the problem on a classical computer before mapping it to a quantum computer. In the same direction, our algorithm decomposes the multidimensional unitary operator and wavefunction into independent, parallel streams of one-dimensional propagators and wavefunctions using Tensor Networks such that an effective Hamiltonian for each dimension can be derived from it. Each one-dimensional system can be mapped to the spin-lattice quantum simulator, where the former determines the governing parameters needed to operate the quantum simulator. These effective one-dimensional subsystems can then be propagated simultaneously and efficiently on the quantum simulator. A shared proton dynamics of a short-strong hydrogen-bonded system on a two-dimensional potential surface has been implemented on IonQ quantum computing hardware to investigate the method's performance.

Mapping multidimensional chemical dynamics problems to a family of hybrid quantum and classical computing environments

Where thousands of chemistry professionals meet to share ideas and advance scientific and technical knowledge. Spring 2023 abstracts, posters, and presentations will launch at the end of March.

ACS Spring 2023

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.