Speaker
Description
The sampling of dense ensembles of self-avoiding polymers provides a paradigmatically hard problem in statistical mechanics and computational physics, even when resorting to minimalistic lattice models. In a series of recent studies, we addressed the question whether these computational limitations can be overcome by quantum annealers or by resorting quantum-inspired algorithms. We show that the reformulation of the sampling problems in terms of q-bits leads to a completely new field-theoretic approach with unexpected deep connections with lattice gauge theory. This greatly reduces the computational cost of sampling compact and/or topologically non-trivial ensembles even when implemented on a classical computer. We also compare the result of implementing our algorithms on classical and quantum hardware.
References
V. Panizza, A. Roggero, P. Hauke, and P. Faccioli, Phys. Rev. Lett. 134 (15), 158101 (2025). F. Slongo, P. Hauke, P. Faccioli, and C. Micheletti, Science Advances 9, eadi0204 (2023). P. Hauke, G. Mattiotti, and P. Faccioli, Phys. Rev. Lett. 126, 028104 (2021)†
| Sessions | Quantum Simulation |
|---|---|
| Invited | Yes |