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Krasecki V, Sharma A, Cavell AC, Forman C, Guo SY, Jensen ET, Smith MA, Czerwinski R, Friederich P, Hickman RJ, Gianneschi N, Aspuru-Guzik A, Cronin L, Goldsmith RH. The Role of Experimental Noise in a Hybrid Classical-Molecular Computer to Solve Combinatorial Optimization Problems. ACS CENTRAL SCIENCE 2023; 9:1453-1465. [PMID: 37521801 PMCID: PMC10375572 DOI: 10.1021/acscentsci.3c00515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Indexed: 08/01/2023]
Abstract
Chemical and molecular-based computers may be promising alternatives to modern silicon-based computers. In particular, hybrid systems, where tasks are split between a chemical medium and traditional silicon components, may provide access and demonstration of chemical advantages such as scalability, low power dissipation, and genuine randomness. This work describes the development of a hybrid classical-molecular computer (HCMC) featuring an electrochemical reaction on top of an array of discrete electrodes with a fluorescent readout. The chemical medium, optical readout, and electrode interface combined with a classical computer generate a feedback loop to solve several canonical optimization problems in computer science such as number partitioning and prime factorization. Importantly, the HCMC makes constructive use of experimental noise in the optical readout, a milestone for molecular systems, to solve these optimization problems, as opposed to in silico random number generation. Specifically, we show calculations stranded in local minima can consistently converge on a global minimum in the presence of experimental noise. Scalability of the hybrid computer is demonstrated by expanding the number of variables from 4 to 7, increasing the number of possible solutions by 1 order of magnitude. This work provides a stepping stone to fully molecular approaches to solving complex computational problems using chemistry.
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Affiliation(s)
- Veronica
K. Krasecki
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Abhishek Sharma
- Department
of Chemistry, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Andrew C. Cavell
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Christopher Forman
- Department
of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
| | - Si Yue Guo
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Evan Thomas Jensen
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Mackinsey A. Smith
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Rachel Czerwinski
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Pascal Friederich
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Riley J. Hickman
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Nathan Gianneschi
- Department
of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Leroy Cronin
- Department
of Chemistry, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Randall H. Goldsmith
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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Hao F, Sun Y, Lin Y. Rough maximal cliques enumeration in incomplete graphs based on partially-known concept learning. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.08.160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Okamoto Y. Maximizing gerrymandering through ising model optimization. Sci Rep 2021; 11:23703. [PMID: 34880318 PMCID: PMC8655093 DOI: 10.1038/s41598-021-03050-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 11/25/2021] [Indexed: 11/09/2022] Open
Abstract
By using the Ising model formulation for combinatorial optimization with 0–1 binary variables, we investigated the extent to which partisan gerrymandering is possible from a random but even distribution of supporters. Assuming that an electoral district consists of square subareas and that each subarea shares at least one edge with other subareas in the district, it was possible to find the most tilted assignment of seats in most cases. However, in cases where supporters' distribution included many enclaves, the maximum tilted assignment was usually found to fail. We also discussed the proposed algorithm is applicable to other fields such as the redistribution of delivery destinations.
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Affiliation(s)
- Yasuharu Okamoto
- System Platform Research Laboratories, NEC Corporation, 1753 Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa, 211-8666, Japan. .,NEC-AIST Quantum Technology Cooperative Research Laboratories, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568, Japan.
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