Unboxing Quantum Black Box Models: Learning Non-Markovian Dynamics

Understand

Characterizing the memory properties of the environment has become critical for the high-fidelity control of qubits and other advanced quantum systems.

• However, current non-Markovian tomography techniques are either limited to discrete superoperators, or they employ machine learning methods, neither of which provide physical insight into the dynamics of the quantum system.
• To circumvent this limitation, we design learning architectures that explicitly encode physical constraints like the properties of completely-positive trace-preserving maps in a differential form.
• This method preserves the versatility of the machine learning approach without sacrificing the efficiency and fidelity of traditional parameter estimation methods.