3 papers
cond-mat.dis-nn2026
Reshaping Global Loop Structure to Accelerate Local Optimization by Smoothing Rugged Landscapes
Timothee Leleu, Sam Reifenstein, Atsushi Yamamura +1
Probabilistic graphical models with frustration exhibit rugged energy landscapes that trap iterative optimization dynamics. These landscapes are shaped not only by local interactio…
cs.LG2026
Neural Ising Machines via Unrolling and Zeroth-Order Training
Sam Reifenstein, Timothee Leleu
We propose a data-driven heuristic for NP-hard Ising and Max-Cut optimization that learns the update rule of an iterative dynamical system. The method learns a shared, node-wise up…
quant-ph2025
A Benchmarking Study of Quantum Algorithms for Combinatorial Optimization
Krishanu Sankar, Artur Scherer, Satoshi Kako +8
We study the performance scaling of three quantum algorithms for combinatorial optimization: measurement-feedback coherent Ising machines (MFB-CIM), discrete adiabatic quantum comp…