Machine-Learned Noise Fingerprinting for Quantum Device Optimization

This research extends Ai & Liu's graph neural network approach for quantum circuit design to tackle the inverse problem of noise source identification. While Reiss & Schechter propose thermal cycling as a method to distinguish TLS noise, and Tsuna et al. analyze 1/f noise impacts, we lack systematic methods to identify specific microscopic noise mechanisms from device behavior. This research would train GNNs on simulated quantum circuits with various known noise sources (TLS, quasiparticles, magnetic flux noise, etc.) to learn the characteristic "fingerprints" each noise type imprints on measurable quantum behaviors like coherence times, gate fidelities, and synchronization patterns. The trained networks could then analyze experimental data from real devices to identify dominant noise mechanisms and suggest targeted mitigation strategies. This moves beyond current noise characterization techniques by leveraging the pattern recognition capabilities of machine learning to solve the complex inverse mapping from device performance to microscopic noise sources. The approach could significantly accelerate the materials and design optimization process that currently relies on trial-and-error methods.

References:

1. Investigation of the Effects of 1/f Noise on Superconducting Circuits. Yusuke Tsuna, Y. Yamanashi, N. Yoshikawa (2020). IEEE transactions on applied superconductivity.
2. Thermal cycling: Evidence for a generalized tunneling model and a tool to distinguish noise sources in quantum circuits. Yigal Reiss, Moshe Schechter (2024). Physical review B.
3. Scalable Parameter Design for Superconducting Quantum Circuits with Graph Neural Networks.. Hao Ai, Yu-xi Liu (2024). Physical Review Letters.