Iyer & Iyengar's quantum fusion framework achieves 20-27 dB SNR gain

The paper by Iyer and Iyengar establishes a fundamental theoretical framework for distributed quantum sensor fusion. They derive a two-parameter family of lower bounds on the mean squared error (MSE) for any estimator, based on entanglement visibility V and the fraction of faulty sensors f/M. The bound combines a term scaling as 1/sqrt(M_eff) for V=0 (standard quantum limit) and a term scaling as 1/M_eff for V=1 (Heisenberg limit), where M_eff accounts for Byzantine fault tolerance via the Brooks-Iyengar overlap function or predictive outlier detection. This continuous interpolation provides a roadmap for achieving optimal precision in noisy, adversarial environments.

The authors validate their theoretical predictions through Monte Carlo simulations with up to 64 quantum sensors, confirming the scaling laws. They also apply the framework to the Intel Berkeley Lab 54-mote classical sensor dataset, showing that entanglement enhancements can yield 20-27 dB SNR improvements per cluster. Interestingly, missing classical sensor data degrades fusion agreement in the same pattern as quantum decoherence, suggesting deep connections between classical and quantum sensor fusion. The work bridges quantum metrology with classical stream-processing architectures like Data-Cleaning Trees and the 80-20 Power Law for scale-invariant clustering, offering practical insights for building fault-tolerant quantum sensor networks.