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 annealer. Our systematic approach offers a promising avenue to harness the rapid development of quantum machines for sampling discrete models of filamentous soft-matter systems.

Micheletti, C., Hauke, P., Faccioli, P. (2021). Polymer Physics by Quantum Computing. PHYSICAL REVIEW LETTERS, 127(8), 1-7 [10.1103/PhysRevLett.127.080501].

Polymer Physics by Quantum Computing

Faccioli, Pietro
2021

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 annealer. Our systematic approach offers a promising avenue to harness the rapid development of quantum machines for sampling discrete models of filamentous soft-matter systems.
Articolo in rivista - Articolo scientifico
Physics - Soft Condensed Matter; Physics - Statistical Mechanics; Quantum Physics
English
2021
127
8
1
7
080501
none
Micheletti, C., Hauke, P., Faccioli, P. (2021). Polymer Physics by Quantum Computing. PHYSICAL REVIEW LETTERS, 127(8), 1-7 [10.1103/PhysRevLett.127.080501].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/405625
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