Krasecki Veronica K, Sharma Abhishek, Cavell Andrew C, Forman Christopher, Guo Si Yue, Jensen Evan Thomas, Smith Mackinsey A, Czerwinski Rachel, Friederich Pascal, Hickman Riley J, Gianneschi Nathan, Aspuru-Guzik Alán, Cronin Leroy, Goldsmith Randall H
Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
Department of Chemistry, University of Glasgow, Glasgow, G12 8QQ, United Kingdom.
ACS Cent Sci. 2023 Jul 14;9(7):1453-1465. doi: 10.1021/acscentsci.3c00515. eCollection 2023 Jul 26.
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 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.
基于化学和分子的计算机可能是现代硅基计算机的有前途的替代方案。特别是混合系统，其中任务在化学介质和传统硅组件之间进行分配，可能会展现出化学方面的优势，如可扩展性、低功耗和真正的随机性。这项工作描述了一种混合经典分子计算机（HCMC）的开发，该计算机在带有荧光读出的离散电极阵列之上进行电化学反应。化学介质、光学读出和电极界面与经典计算机相结合，形成一个反馈回路，以解决计算机科学中的几个典型优化问题，如数划分和质因数分解。重要的是，HCMC通过在光学读出中建设性地利用实验噪声（这是分子系统的一个里程碑）来解决这些优化问题，而不是用于生成随机数。具体而言，我们表明，在存在实验噪声的情况下，被困在局部最小值的计算能够始终收敛到全局最小值。通过将变量数量从4个扩展到7个，将可能的解决方案数量增加1个数量级，证明了混合计算机的可扩展性。这项工作为使用化学方法完全分子化地解决复杂计算问题提供了一块垫脚石。
