A Eureka machine that thinks like nature and explores what AI cannot

Neuromorphic Ising machine implemented on an FPGA board rapidly explores rugged energy landscapes with exponentially many competing possibilities, enabling fast discovery of near-optimal solutions for complex optimisation problems such as protein folding, where the search evolves from an unfolded chain through intermediate molten-globule states toward the most stable folded structure.

The hardest computational problems are not waiting for faster chips – they are waiting for machines that compute in a fundamentally different way.

A multi-institution team, emerging from the Telluride Neuromorphic and Cognition Engineering workshop in Colorado, and the Bangalore Neuromorphic Engineering Workshop (BNEW) at IISc, has built a neuromorphic computer that combines quantum-tunnelling physics with a brain-inspired architecture to find solutions to hard mathematical problems. Published in Nature Communications, the work introduces a new direction in quantum-inspired computing built on CMOS technology.

Today, AI models may have the capability to write novels and even steer a spacecraft. But give them a logistics network, a microchip to route, or a cryptographic lock, and they stall. These are combinatorial problems – among the most consequential unsolved frontiers in computing. The new study suggests that a neuromorphic autoencoder with a Fowler-Nordheim annealer can solve these problems at scale, with a guarantee of asymptotic convergence to the optimal solution.