Matches in SemOpenAlex for { <https://semopenalex.org/work/W3133661630> ?p ?o ?g. }
- W3133661630 abstract "Computationally intractable tasks are often encountered in physics and optimization. Such tasks often comprise a cost function to be optimized over a so-called feasible set, which is specified by a set of constraints. This may yield, in general, to difficult and non-convex optimization tasks. A number of standard methods are used to tackle such problems: variational approaches focus on parameterizing a subclass of solutions within the feasible set; in contrast, relaxation techniques have been proposed to approximate it from outside, thus complementing the variational approach by providing ultimate bounds to the global optimal solution. In this work, we propose a novel approach combining the power of relaxation techniques with deep reinforcement learning in order to find the best possible bounds within a limited computational budget. We illustrate the viability of the method in the context of finding the ground state energy of many-body quantum systems, a paradigmatic problem in quantum physics. We benchmark our approach against other classical optimization algorithms such as breadth-first search or Monte-Carlo, and we characterize the effect of transfer learning. We find the latter may be indicative of phase transitions, with a completely autonomous approach. Finally, we provide tools to generalize the approach to other common applications in the field of quantum information processing." @default.
- W3133661630 created "2021-03-15" @default.
- W3133661630 creator A5044744258 @default.
- W3133661630 creator A5051113581 @default.
- W3133661630 creator A5065818921 @default.
- W3133661630 creator A5069043772 @default.
- W3133661630 creator A5078663797 @default.
- W3133661630 date "2023-02-10" @default.
- W3133661630 modified "2023-10-09" @default.
- W3133661630 title "Certificates of quantum many-body properties assisted by machine learning" @default.
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