Quantum-Enhanced Distributed Sensor Fusion: Lower Bounds on Aggregation from Projection Noise to Heisenberg-Limited Byzantine-Tolerant Networks

2026-05-19

AI Summary

This study investigates the fundamental precision limits of distributed quantum sensor fusion under the joint influence of Byzantine faults and quantum decoherence. It proposes a unified framework for the mean squared error lower bound, jointly modeling entanglement visibility and fault fraction for the first time, and derives a performance boundary that continuously interpolates between the standard quantum limit and the Heisenberg limit—thereby bridging quantum metrology with classical stream processing architectures. The approach integrates Brooks–Iyengar interval overlap functions, SPOTLESS spatiotemporal validation, and Data-Cleaning Trees, enhanced by 80–20 power-law clustering to improve robustness. Monte Carlo simulations confirm the theoretical scaling laws, and experiments on the Intel Berkeley dataset demonstrate 20–27 dB signal-to-noise ratio gains per cluster, revealing an equivalence between classical data loss and quantum decoherence in degrading fusion consistency.

Abstract

We derive unified lower bounds on the mean squared error (MSE) of distributed quantum sensor fusion under Byzantine faults and decoherence. Building on the classical Brooks-Iyengar overlap function and its vector extension, the predictive outlier model for virtual sensor tracking, and SPOTLESS spatial-temporal verification, we establish a two-parameter family of bounds indexed by entanglement visibility V and fault fraction f/M. For M quantum sensors with N atoms each and sensitivity eta, the MSE of any estimator satisfies MSE >= (1-V^2)/(4*N*eta^2*M_eff) + V^2/(4*N*eta^2*M_eff^2), where M_eff = M-2f under Brooks-Iyengar Byzantine fault tolerance and M_eff = M-f when predictive outlier detection successfully identifies faulty sensors. The bound interpolates continuously between the standard quantum limit (V=0, scaling as 1/sqrt(M_eff)) and the Heisenberg limit (V=1, scaling as 1/M_eff). Monte Carlo simulations with up to 64 sensors validate the theoretical scaling laws. Validation on the Intel Berkeley Lab 54-mote dataset with spatial clustering demonstrates 20-27 dB SNR improvement from entanglement per cluster, and reveals that missing classical sensor data degrades fusion agreement in the same pattern as quantum decoherence. The framework bridges quantum metrology with classical stream-processing architectures including Data-Cleaning Trees and the 80-20 Power Law for scale-invariant clustering.

Problem

• quantum sensor fusion
• Byzantine faults
• decoherence
• mean squared error
• Heisenberg limit

Innovation

• quantum sensor fusion
• Byzantine fault tolerance
• Heisenberg limit
• entanglement visibility
• distributed quantum metrology