Quantum advantage in solving physical problems is still hard to assess due to hardware limitations. However, algorithms designed for quantum computers may engender transformative frameworks for modeling and simulating paradigmatically hard systems. Here, we show that the quadratic unconstrained binary optimization encoding enables tackling classical many-body systems that are challenging for conventional Monte Carlo. Specifically, in self-assembled melts of rigid lattice ring polymers, the combination of high density, chain stiffness, and topological constraints results in divergent autocorrelation times for real-space Monte Carlo. Our quantum-inspired encoding overcomes this problem and enables sampling melts of lattice rings with fixed curvature and compactness, unveiling counterintuitive topological effects. Tackling the same problems with the D-Wave quantum annealer leads to substantial performance improvements and advantageous scaling of sampling computational cost with the size of the self-assembled ring melts.

Slongo, F., Hauke, P., Faccioli, P., Micheletti, C. (2023). Quantum-inspired encoding enhances stochastic sampling of soft matter systems. SCIENCE ADVANCES, 9(43) [10.1126/sciadv.adi0204].

Quantum-inspired encoding enhances stochastic sampling of soft matter systems

Faccioli P.
;
2023

Abstract

Quantum advantage in solving physical problems is still hard to assess due to hardware limitations. However, algorithms designed for quantum computers may engender transformative frameworks for modeling and simulating paradigmatically hard systems. Here, we show that the quadratic unconstrained binary optimization encoding enables tackling classical many-body systems that are challenging for conventional Monte Carlo. Specifically, in self-assembled melts of rigid lattice ring polymers, the combination of high density, chain stiffness, and topological constraints results in divergent autocorrelation times for real-space Monte Carlo. Our quantum-inspired encoding overcomes this problem and enables sampling melts of lattice rings with fixed curvature and compactness, unveiling counterintuitive topological effects. Tackling the same problems with the D-Wave quantum annealer leads to substantial performance improvements and advantageous scaling of sampling computational cost with the size of the self-assembled ring melts.
Articolo in rivista - Articolo scientifico
Quantum computing; statistical mechanics; polymer melts
English
25-ott-2023
2023
9
43
eadi0204
open
Slongo, F., Hauke, P., Faccioli, P., Micheletti, C. (2023). Quantum-inspired encoding enhances stochastic sampling of soft matter systems. SCIENCE ADVANCES, 9(43) [10.1126/sciadv.adi0204].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/448864
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