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Description
QSOpt (Quantum Sensing Optimization) is an end-to-end differentiable simulation and machine-learning optimization framework for open quantum networks composed of superconducting qubits, bosonic modes and input-output channels with user-defined interactions.
The advancements in quantum technologies have sparked interest in employing quantum systems as sensors, with superconducting quantum circuits emerging as
a flexible tool for metrology and quantum sensing, with application in fundamental physics like light dark-matter and axions detection. A promising architecture employs networks of superconducting qubits coupled to microwave bosonic modes. However, accurately modeling and optimizing these systems under realistic noise conditions requires numerical methods beyond tractable analytical approaches.
QSOpt integrates the QuTiP quantum simulation library with a JAX backend, enabling gradient-based optimization of parametrized quantum circuits for state preparation and readout, together with sweeps over hardware parameters such as couplings and dispersive shifts. This enables systematic exploration of sensing strategies and maximization of sensing performance in multi-qubit architectures under realistic conditions, while facilitating the discovery of previously unexplored
sensing protocols.