Autonomous calibration of quantum networks using Bayesian optimization

Quantum networks are important for distributed quantum computing, sensor networks and quantum cryptography. While small-scale, optical fiber-based quantum networks appear to be ready for near-term deployment, there remains important challenges in establishing large-scale quantum networks. One fundamental practical problem arises from the way optical components, such as the fiber transmission channel, dynamically vary the polarization and arrival time of photons due to temperature fluctuations. Since quantum networks rely on photon interference, precise control of the properties of photons is necessary for deploying a functional quantum network. Brute-force algorithms are often used to sequentially calibrate each degree of freedom however this approach becomes increasingly challenging for lossy and long-distance networks where the photon detection rate is low. In this talk, we present a Bayesian optimization algorithm for automating the calibration of single-photon states. To validate our approach, we present a proof-of-concept demonstration of the optimization of an experimental Hong-Ou-Mandel measurement scheme. We envision that our methodology opens up the possibility of fast and reliable calibration of quantum optical experiments for a wide variety of applications.