New Study Uses Machine Learning to Bridge the Reality Gap in Quantum Devices

A groundbreaking study by the University of Oxford introduces a novel machine learning methodology to address the 'reality gap' in quantum devices the discrepancy between their predicted and actual performances. This research, published in Physical Review X, showcases a 'physics-informed' machine learning technique to deduce the internal disorder of quantum devices, influencing electron flow. The study's approach provides a more accurate model of quantum device behavior by inferring nanoscale imperfections indirectly, a significant step forward in quantum computing, simulation, and materials engineering. By bridging this reality gap, the study opens new avenues for optimizing quantum device fabrication and performance, promising advancements in a range of applications from drug discovery to artificial intelligence.