Learning efficient erasure protocols for an underdamped memory
Journal Article
·
· Physical Review. E
- Centre National de la Recherche Scientifique (CNRS) (France); École normale supérieure de Lyon (France)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry
Here we apply evolutionary reinforcement learning to a simulation model to identify efficient time-dependent erasure protocols for a physical realization of a 1-bit memory using an underdamped mechanical cantilever. We show that these protocols, when applied to the cantilever in the laboratory, are considerably more efficient than our best hand-designed protocols. The learned protocols allow reliable high-speed erasure by minimizing the heating of the memory during its operation. More generally, the combination of methods used here opens the door to the rational design of efficient protocols for various physics applications.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 2565640
- Alternate ID(s):
- OSTI ID: 2554087
- Journal Information:
- Physical Review. E, Journal Name: Physical Review. E Journal Issue: 4 Vol. 111; ISSN 2470-0045
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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