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Agenda


Adaptive Ising Machines: a paradigm combining oscillatory dynamics and probabilistic sampling

​Mercredi 28 janvier 2026 à 14:00, Salle de séminaire 445, bâtiment 1005, CEA-Grenoble

Publié le 28 janvier 2026
Giovanni Finocchio
University of Messina, Italy
The first part of the talk will focus on probabilistic computing which is one direction to implement Ising Machines with Probabilistic computing that is a computational paradigm using probabilistic bits (p-bits), unit in the middle between standard bit and q-bits. I will show how to map hard combinatorial optimization problems (Max-Sat, Max-Cut, etc) into Ising machine and how to implement those in spintronic technology. I will present new directions in this field considering the concept of extended probabilistic variables such as p-dit and p-int. I will then discuss oscillatory Ising Machines dynamics with the aim of introducing the concept of Adaptive Ising Machines. For doing that, first I will discuss the universal theory of phase auto-oscillators driven by a bi-harmonic signal (having frequency components close to single and double of the free-running oscillator frequency) with noise showing how deterministic phase locking and stochastic phase slips can be continuously tuned by varying the relative amplitudes and frequencies of the driving components. I will then focus on how to use spin-torque nano-oscillators for implementing deterministic, probabilistic computing and dual-mode operation of Adaptive Ising Machines that dynamically combines both regimes within the same hardware platform by properly tuning noise strength and a bi-harmonic excitation. Benchmarking on different classes of combinatorial optimization problems, the CoIM exhibits complementary performance as compared to OIMs and probabilistic Ising machines PIMs, with adaptability to the specific problem class. This work introduces the first oscillator-based Ising machine capable of transitioning between deterministic and probabilistic computation, opening a path toward scalable, CMOS-compatible hardware for hybrid optimization and inference. This work was supported under the project number 101070287 — SWAN-on-chip — HORIZON-CL4-2021-DIGITAL-EMERGING-01, the project PRIN 2020LWPKH7 funded by the Italian Ministry of University and Research and by the PETASPIN association (www.petaspin.com) and it has been also funded by European Union (NextGeneration EU), through the MUR-PNRR project SAMOTHRACE (ECS00000022).
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More information :https://www.spintec.fr/seminar-adaptive-ising-machines-a-paradigm-combining-oscillatory-dynamics-and-probabilistic-sampling/​​

Visioconference :https://univ-grenoble-alpes-fr.zoom.us/j/98769867024?pwd=dXNnT3RMeThjYStybGVQSUN0TVdJdz09​​
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