Quantum Neural Network – connecting Quantum and Brain with Optics: Professor Yoshihisa Yamamoto, Stanford University
Quantum Neural Network – connecting Quantum and Brain with Optics
Professor Yoshihisa Yamamoto
Director of Physics & Informatics Laboratories, NTT Research
Professor (Emeritus) of Electrical Engineering and Applied Physics, Stanford University
Stanford University
Part of the John A. Lynch Lecture Series, College of Science
Combinatorial optimization problems are ubiquitous in our modern life. Classical examples include lead optimization in drug and biocatalyst discovery, resource optimization in wireless communications, sparse coding for compressed sensing, deep learning in artificial intelligence and fintech. These optimization problems can be easily mapped to either Ising model, XY model or k-SAT problem, which is a main reason why various Ising, XY and SAT solvers have been proposed and implemented in the past ten years.
- 1 G. Björk and Y. Yamamoto, Phys. Rev. A 37, 4229-4239 (June 1988).
- 2 K. Watanabe and Y. Yamamoto, Phys. Rev. A 38, 3556-3565 (October 1988).
- 3 D. K. Serkland et al., Opt. Lett. 20, 1649-1651 (August 1995).
- 4 Z. Wang et al., Phys. Rev. A, 88, 063853 (December 2013).
- 5 A. Marandi et al., Nature Photonics 8, 937-942 (October 2014).
- 6 T. Inagaki et al., Nature Photonics 10, 415-419 (June 2016).
- 7 P. L. McMahon et al., Science 354, 614-617 (October 2016).
- 8 T. Inagaki et al., Science 354, 603-606 (October 2016).
Originally published at physics.nd.edu.