Artificial Brain Networks Simulated with New Quantum Materials

(Phys.org) A team of UC San Diego researchers and colleagues at Purdue University have now simulated the foundation of new types of artificial intelligence computing devices that mimic brain functions. By combining new supercomputing materials with specialized oxides, the researchers successfully demonstrated the backbone of networks of circuits and devices that mirror the connectivity of neurons and synapses in biologically based neural networks.
Neuromorphic computing based on quantum materials, which display quantum-mechanics-based properties, allow scientists the ability to move beyond the limits of traditional semiconductor materials. This advanced versatility opens the door to new-age devices that are far more flexible with lower energy demands than today’s devices. Some of these efforts are being led by Department of Physics Assistant Professor Alex Frañó and other researchers in UC San Diego’s Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C), a Department of Energy-supported Energy Frontier Research Center.
The researchers’ innovation was based on joining two types of quantum substances—superconducting materials based on copper oxide and metal insulator transition materials that are based on nickel oxide. They created basic “loop devices” that could be precisely controlled at the nano-scale with helium and hydrogen, reflecting the way neurons and synapses are connected. Adding more of these devices that link and exchange information with each other, the simulations showed that eventually they would allow the creation of an array of networked devices that display emergent properties like an animal’s brain.