PHI Lab Team

Timothee Leleu

Scientist

Timothée Leleu is a Group Head/Senior Research Scientist in NTT Research’s PHI Lab. Prior to joining the company, he worked on neuromorphic computing algorithms and architecture at the University of Tokyo. To date, his most significant contribution to the field of coherent Ising machines is the invention of the chaotic amplitude control (CAC) algorithm. In the PHI Lab, Timothee serves as the head of the Algorithms & Application group.

Videos

Play Video

Using Principles of Neural Networks to Increase the Efficiency of Ising Machine Simulators

September 21, 2020

Play Video

A Fast, Scalable, and Reconfigurable Simulation Platform for the Coherent Ising Machine

September 21, 2021

Publications

  • Coherent SAT solvers: a tutorial

    By Sam Reifenstein, Timothee Leleu, Timothy McKenna, Marc Jankowski, Myoung-Gyun Suh, Edwin Ng, Farad Khoyratee, Zoltan Toroczkai & Yoshihisa Yamamoto

    dvances in Optics and Photonics 2023

  • Experimental observation of chimera states in spiking neural networks based on degenerate optical parametric oscillators

    By Tumi Makinwa, Kensuke Inaba, Takahiro Inagaki, Yasuhiro Yamada, Timothée Leleu, Toshimori Honjo, Takuya Ikuta, Koji Enbutsu, Takeshi Umeki, Ryoichi Kasahara, Kazuyuki Aihara & Hiroki Takesue

    Communications Physics 2023

  • Mathematical aspects of the Digital Annealer’s simulated annealing algorithm

    By Tumi Makinwa, Kensuke Inaba, Takahiro Inagaki, Yasuhiro Yamada, Timothée Leleu, Toshimori Honjo, Takuya Ikuta, Koji Enbutsu, Takeshi Umeki, Ryoichi Kasahara, Kazuyuki Aihara & Hiroki Takesue

    Adv. Quantum Technol. 2023

Destabilization of Local Minima in Analog Spin Systems by Correction of Amplitude Heterogeneity

By Timothée Leleu, Yoshihisa Yamamoto, Peter L. McMahon & Kazuyuki Aihara1

Physical Review Letters 2019

  • Overlapping and non-interfering waves of bursts

    By Timothée Leleu & Kazuyuki Aihara

    Book Chapter, Cognitive Phase Transitions in the Cerebral Cortex-Enhancing the Neuron Doctrine by Modeling Neural Fields, Springer 2016