  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.
