Polymer Physics by Quantum Computing

TL;DR

This paper introduces a quantum annealing-based formalism using interacting binary tensors to efficiently sample dense polymer mixtures, addressing a computationally hard problem in polymer physics.

Contribution

It develops a general tensor-based approach compatible with quantum annealing to model complex polymer properties and generate microstates.

Findings

Successfully sampled polymer mixtures from low to high densities

Demonstrated the method on D-Wave quantum computer

Showed potential for quantum computers in soft-matter modeling

Abstract

Sampling equilibrium ensembles of dense polymer mixtures is a paradigmatically hard problem in computational physics, even in lattice-based models. Here, we develop a formalism based on interacting binary tensors that allows for tackling this problem using quantum annealing machines. Our approach is general in that properties such as self-avoidance, branching, and looping can all be specified in terms of quadratic interactions of the tensors. Microstates realizations of different lattice polymer ensembles are then seamlessly generated by solving suitable discrete energy-minimization problems. This approach enables us to capitalize on the strengths of quantum annealing machines, as we demonstrate by sampling polymer mixtures from low to high densities, using the D-Wave quantum computer. Our systematic approach offers a promising avenue to harness the rapid development of quantum computers for sampling discrete models of filamentous soft-matter systems.