AI RESEARCH
Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
arXiv CS.LG
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ArXi:2604.24397v1 Announce Type: cross In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error mitigation strategies. We explore transfer learning approaches to apply noise models learned on one quantum device to a different device with the help of a small amount of data. We create a real-hardware dataset from two IBM quantum devices, ibm_fez (source) and ibm_marrakesh (target), comprising 170 noisy and ideal circuit output distributions, with device calibration features added.