Lecture: "Quantum Neural Network — Connecting Quantum and Brain with Optics"

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Location: 129 DeBartolo Hall (View on map.nd.edu)

Professor Yoshihisa Yamamoto
Director of Physics & Informatics Laboratories, NTT Research
Professor (Emeritus) of Electrical Engineering and Applied Physics, 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. 

We have focused on the network of optical parametric oscillators to construct coherent Ising machines, XY machines and SAT solvers. An optical parametric oscillator (OPO) operates as a quantum analog device at below oscillation threshold and also functions as a classical digital device at above oscillation threshold. We need not only quantum computational resources, such as quantum correlation and quantum suppression of chaos, but also classical computational resources, such as spontaneous symmetry breaking and exponential amplitude amplification, to build an efficient optimizer. An OPO is almost a unique choice of device to realize such quantum and classical computational resources simultaneously at room temperatures. 
 
A study on quantum state engineering in an OPO with measurement-feedback control was dated back to 1980s [1,2]. A PPLN waveguide OPO device was identified as a stable and efficient generator for squeezed vacuum state pulses at communication wavelengths in 1990s 3. An idea of using the pitchfork bifurcation and spontaneous symmetry breaking at OPO threshold as an irreversible decision making process was proposed in 2013 4 and experimentally implemented [5,6]. A scalable architecture based on measurement-feedback coupling scheme was subsequently demonstrated [7,8]. In this talk, we will discuss the principles of quantum-classical crossover and future prospects of quantum neural network. 
  1. 1  G. Björk and Y. Yamamoto, Phys. Rev. A 37, 4229-4239 (June 1988). 
  2. 2  K. Watanabe and Y. Yamamoto, Phys. Rev. A 38, 3556-3565 (October 1988). 
  3. 3  D. K. Serkland et al., Opt. Lett. 20, 1649-1651 (August 1995). 
  4. 4  Z. Wang et al., Phys. Rev. A, 88, 063853 (December 2013). 
  5. 5  A. Marandi et al., Nature Photonics 8, 937-942 (October 2014). 
  6. 6  T. Inagaki et al., Nature Photonics 10, 415-419 (June 2016). 
  7. 7  P. L. McMahon et al., Science 354, 614-617 (October 2016). 
  8. 8  T. Inagaki et al., Science 354, 603-606 (October 2016). 

Originally published at physics.nd.edu.