Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Leleu, Timothée"'
Autor:
Reifenstein, Sam, Leleu, Timothée
In this note we study an iterative belief propagation (IBP) algorithm and demonstrate it's ability to solve sparse combinatorial optimization problems. Similar to simulated annealing (SA), our IBP algorithm attempts to sample from the Boltzmann distr
Externí odkaz:
http://arxiv.org/abs/2411.00135
Autor:
Leleu, Timothée, Reifenstein, Samuel
We propose a general framework for a hybrid continuous-discrete algorithm that integrates continuous-time deterministic dynamics with Metropolis-Hastings steps to combine search dynamics with and without detailed balance. Our purpose is to study the
Externí odkaz:
http://arxiv.org/abs/2410.22625
We propose a novel algorithm that extends the methods of ball smoothing and Gaussian smoothing for noisy derivative-free optimization by accounting for the heterogeneous curvature of the objective function. The algorithm dynamically adapts the shape
Externí odkaz:
http://arxiv.org/abs/2405.01731
Autor:
Makinwa, Tumi, Inaba, Kensuke, Inagaki, Takahiro, Yamada, Yasuhiro, Leleu, Timothee, Honjo, Toshimori, Ikuta, Takuya, Enbutsu, Koji, Umeki, Takeshi, Kasahara, Ryoichi, Aihara, Kazuyuki, Takesue, Hiroki
We experimentally demonstrate that networks of identical photonic spiking neurons based on coupled degenerate parametric oscillators can show various chimera states, in which, depending on their local synchronization and desynchronization, different
Externí odkaz:
http://arxiv.org/abs/2209.12087
We propose a network of open-dissipative quantum oscillators with optical error correction circuits. In the proposed network, the squeezed/anti-squeezed vacuum states of the constituent optical parametric oscillators below the threshold establish qua
Externí odkaz:
http://arxiv.org/abs/2108.07369
Autor:
Inagaki, Takahiro, Inaba, Kensuke, Leleu, Timothée, Honjo, Toshimori, Ikuta, Takuya, Enbutsu, Koji, Umeki, Takeshi, Kasahara, Ryoichi, Aihara, Kazuyuki, Takesue, Hiroki
Publikováno v:
Nat Commun 12, 2325 (2021)
Nonlinear dynamics of spiking neural networks has recently attracted much interest as an approach to understand possible information processing in the brain and apply it to artificial intelligence. Since information can be processed by collective spi
Externí odkaz:
http://arxiv.org/abs/2009.11454
Autor:
Leleu, Timothee, Khoyratee, Farad, Levi, Timothee, Hamerly, Ryan, Kohno, Takashi, Aihara, Kazuyuki
The development of physical simulators, called Ising machines, that sample from low energy states of the Ising Hamiltonian has the potential to drastically transform our ability to understand and control complex systems. However, most of the physical
Externí odkaz:
http://arxiv.org/abs/2009.04084
Publikováno v:
Sci Rep 10, 21794 (2020)
Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs." To implement RC in edg
Externí odkaz:
http://arxiv.org/abs/2006.06218
Autor:
Kako, Satoshi, Leleu, Timothée, Inui, Yoshitaka, Khoyratee, Farad, Reifenstein, Sam, Yamamoto, Yoshihisa
Publikováno v:
Adv. Quantum Technol. 2020, 2000045
A non-equilibrium open-dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems
Externí odkaz:
http://arxiv.org/abs/2005.10895
Publikováno v:
Phys. Rev. Lett. 122, 040607 (2019)
The relaxation of binary spins to analog values has been the subject of much debate in the field of statistical physics, neural networks, and more recently quantum computing, notably because the benefits of using an analog state for finding lower ene
Externí odkaz:
http://arxiv.org/abs/1810.12565