Eli Yablonovitch, UC-Berkeley
Host: Mark Raizen
Physics does Optimization (for Free); A New Approach Toward Computation
Optimization is vital to Engineering, Artificial Intelligence, and to many areas of Science. Mathematically, we usually employ steepest-descent, or other digital algorithms. But, every inequality in Physics, performs optimization in the normal course of dynamical evolution–for free. Nature provides us with the following optimization principles:
1. The Principle of Least Action;
2. The Variational Principle of Quantum Mechanics;
3. The Principle of Minimum Entropy Generation;
4. The First Mode to Threshold method;
5. The Principle of Least Time;
6. The Adiabatic Evolution method;
7. Quantum Annealing
In effect, Physics can provide machines which solve digital optimization problems much faster than any conventional computer. Of these physics principles, “Minimum Entropy Generation” in the form of bistable electrical or optical circuits is particularly adaptable  toward offering digital Optimization. For example, we provide the electrical circuit which can address the challenging Ising problem, binary magnet energy minimization.
Since Onsager,  (1931, Nobel Prize 1968) introduced the Principle of Minimum Entropy Generation we call this Onsager Computing, as opposed to conventional Von Neumann Computing. In ref. 1 we showed that many of the schemes for Physics-Based-Optimization, reduce to the Onsager Principle. Electrical Onsager Computers run ~10000 times faster have ~10000 times less energy-to-solution, than conventional machines. Furthermore, optical Onsager machines provide an additional 1000 times increase in speed.
References: Sri K. Vadlamani, T. Patrick Xiao and Eli Yablonovitch, “Physics Successfully Implements Lagrange Multiplier Optimization” Proceedings of National Academy of Sciences USA 117 26639-26650 (2020). https://doi.org/10.1073/pnas.2015192117
 L. Onsager, “Reciprocal Relations in Irreversible Processes. II.” Physical Review, vol. 38, pp. 2265-2279, (Dec. 1931).