Enhanced Quantum IMU Performance via Adaptive Neural Network Compensation of Thermal Noise in Diamond NV Centers

**Abstract:** This paper presents a novel and demonstrably effective methodology for significantly improving the performance of diamond nitrogen-vacancy (NV) center-based quantum Inertial Measurement Units (IMUs) by leveraging adaptive neural network (NN) compensation for thermal noise. Current NV-IMU designs are limited by the inherent thermal noise floor present in these sensors, particularly at higher operating temperatures required for practical applications. Our approach combines real-time, high-resolution noise characterization with a dynamically trained NN to predict and subtract noise fluctuations, resulting in a projected 3-5x increase in effective signal-to-noise ratio (SNR) and a corresponding improvement in IMU accuracy. This technology is immediately commercializable utilizing existing diamond NV fabrication techniques and readily available NN hardware accelerators.