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Aspuru-Guzik-Molecular Computing for ChemRxiv-2019-11-04.pdf (1.58 MB)

A Molecular Computing Approach to Solving Optimization Problems via Programmable Microdroplet Arrays

preprint
submitted on 05.11.2019, 01:06 and posted on 12.11.2019, 21:13 by Si Yue Guo, Pascal Friederich, Yudong Cao, Tony Wu, Christopher Forman, Douglas Mendoza, Matthias Degroote, Andrew Cavell, Veronica Krasecki, Riley Hickman, Abhishek Sharma, Leroy Cronin, Nathan Gianneschi, Randall Goldsmith, Alan Aspuru-Guzik
The search for novel forms of computing that show advantages as alternatives to the dominant von-Neuman model-based computing is important as it will enable different classes of problems to be solved. By using droplets and room-temperature processes, molecular computing is a promising research direction with potential biocompatibility and cost advantages. In this work, we present a new approach for computation using a network of chemical reactions taking place within an array of spatially localized droplets whose contents represent bits of information. Combinatorial optimization problems are mapped to an Ising Hamiltonian and encoded in the form of intra- and inter- droplet interactions. The problem is solved by initiating the chemical reactions within the droplets and allowing the system to reach a steady-state; in effect, we are annealing the effective spin system to its ground state. We propose two implementations of the idea, which we ordered in terms of increasing complexity. First, we introduce a hybrid classical-molecular computer where droplet properties are measured and fed into a classical computer. Based on the given optimization problem, the classical computer then directs further reactions via optical or electrochemical inputs. A simulated model of the hybrid classical-molecular computer is used to solve boolean satisfiability and a lattice protein model. Second, we propose architectures for purely molecular computers that rely on pre-programmed nearest-neighbour inter-droplet communication via energy or mass transfer.

Funding

DARPA W911NF-18-2-0036

Marie Skłodowska-Curie 795206

ERC Advanced Grant (Smart-POM)

EPSRC EP/R009902/1

SciNet HPC Consortium

Dr. Anders G. Frøseth

History

Email Address of Submitting Author

siyue.guo@utoronto.ca

Institution

University of Toronto

Country

Canada

ORCID For Submitting Author

0000-0002-3966-6657

Declaration of Conflict of Interest

The University of Toronto has filed a provisional application or a US patent based on the technology described in this paper, naming S.Y.G., P.F., Y.C., T.W., C.J.F., A.S., L.C., N.G., R.H.G., and A.A.-G. as inventors.

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