IBM and MIT researchers unveiled a quantum algorithm optimized by AI, achieving a 100x speedup in solving complex optimization problems like protein folding. The AI trains quantum circuits to minimize errors caused by quantum noise, a persistent hurdle in practical quantum computing.
Technical Insight:
- The AI uses reinforcement learning to dynamically adjust qubit parameters during calculations, counteracting decoherence (loss of quantum state).
- Demonstrated on IBM’s 127-qubit Eagle processor, the system solved a logistics routing problem for Walmart in 4 minutes—previously a 7-hour task.
Applications:
- Supply Chains: Optimizing delivery routes in real time during disruptions (e.g., port closures).
- Material Science: Accelerating discovery of high-temperature superconductors.
- Finance: Portfolio risk analysis under volatile market conditions.
Challenges: Scalability remains limited to mid-sized quantum processors, and energy costs for AI-quantum hybrid systems are high.