1. A molecular computing approach to solving optimization problems via programmable microdroplet arrays
- Author
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Douglas Mendoza, Alán Aspuru-Guzik, Nathan C. Gianneschi, Veronica K. Krasecki, Andrew C. Cavell, Pascal Friederich, Matthias Degroote, Si Yue Guo, Leroy Cronin, Chris Forman, Randall H. Goldsmith, Yudong Cao, Tony C. Wu, Riley J. Hickman, and Abhishek Sharma
- Subjects
0303 health sciences ,Optimization problem ,Computer science ,Monte Carlo method ,Binary number ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Computational science ,03 medical and health sciences ,symbols.namesake ,Scalability ,symbols ,Combinatorial optimization ,General Materials Science ,Ising model ,0210 nano-technology ,Hamiltonian (control theory) ,030304 developmental biology ,Von Neumann architecture - Abstract
Summary The search for novel forms of computing to the dominant von Neumann model-based approach is important as it will enable different classes of problems to be solved. Molecular computers are a promising alternative to semiconductor-based computers given their potential biocompatibility and cost advantages. The vast space of chemical reactions makes molecules a tunable, scalable, and energy-efficient computational vehicle. In molecular computers, memory and processing units can be combined into a single, inherently parallelized device. Here, we present a microdroplet array molecular computer to solve combinatorial optimization problems by employing an Ising Hamiltonian to map problems heuristically to droplet-droplet interactions. The droplets represent binary digits and problems are encoded in intra- and inter-droplet reactions. We propose two implementations: first, a hybrid classical-molecular computer that enforces inter-droplet constraints in a classical computer and, second, a purely molecular computer where the problem is entirely pre-programmed in the nearest-neighbor droplet reactions.
- Published
- 2021