We found a new kind of computer that doesn't care how big a problem is—it takes the same amount of time to solve a massive puzzle as it does a tiny one.
March 27, 2026
Original Paper
Optimality and annealing path planning of dynamical analog solvers
arXiv · 2603.13778
The Takeaway
Most computational tasks get exponentially slower as they grow, but by mimicking how atoms naturally settle into magnetic patterns, this system reaches near-perfect solutions in 'constant time.' This discovery challenges the fundamental belief that certain complex math problems must always get slower as they scale up.
From the abstract
Recently proposed analog solvers based on dynamical systems, such as Ising machines, are promising platforms for large-scale combinatorial optimization. Yet, given the heuristic nature of the field, there is very limited insight on optimality guarantees of the solvers, as well as how parameter schedules shape dynamics and outcomes. Here, we develop a dynamical mean-field framework to analyze Ising-machine dynamics for finding the ground state energy of the Sherrington-Kirkpatrick(SK) model of sp