Researchers Explore Quantum Inspired AI Repair

Insider Brief

• Researchers from QDeep and ITMO University developed Tree-Ising, a method that repairs repetitive reasoning in small language models through targeted changes to their internal weights.
• Running on conventional GPUs, the method achieved 55.1% accuracy on MATH-500 for one model and increased another model’s GSM8K accuracy from 21.6% to 69.4% under specific test conditions.
• The team executed an eight-variable repair subproblem on an IBM quantum processor as a proof of concept, without demonstrating a quantum advantage or a complete model repair on quantum hardware.

Researchers have developed a method that repairs repetitive reasoning in small language models by changing selected internal weights, with a separate quantum hardware experiment testing a possible route for future work.

The study, by Hadi Salloum of QDeep in Riyadh, Saudi Arabia, and Ammar Ali of ITMO University’s Lab for Efficient AI and Data, introduces Tree-Ising, a system for turning model repairs into mathematical optimization problems. The researchers describe the method in a preprint posted to arXiv.

The main repairs ran on conventional graphics processors. One solver uses simulated bifurcation, a quantum-inspired computing method that runs on classical hardware. The quantum experiment addressed only a small part of a repair problem and did not demonstrate a quantum advantage.