Bayesian Optimization of Fisher Information in Nonlinear Multiresonant Quantum Photonics Gyroscopes

We propose an on-chip gyroscope based on nonlinear multiresonant optics in a thin film resonator that combines high sensitivity, compact form factor, and low power consumption simultaneously. We theoretically analyze a novel metric - Fisher Information capacity of a multiresonant nonlinear photonic cavity - to fully characterize the sensitivity of our gyroscope under fundamental quantum noise conditions. Leveraging Bayesian optimization techniques, we directly maximize the nonlinear multiresonant Fisher Information. Our optimization approach orchestrates a harmonious convergence of multiple physical phenomena - including noise squeezing, nonlinear wave mixing, nonlinear critical coupling, and noninertial signals - all encapsulated within a single sensor-resonator, thereby significantly augmenting sensitivity. We show that improvement is possible over the shot-noise limited linear gyroscope with the same footprint, intrinsic quality factors, and power budget.