Speaker
Description
Quantum process tomography (QPT) obtains the representation of a quantum process using experimentally obtained measurement data. We can cast the QPT problem into a learning task where machine learning methods have been recently successful in using generative models for QPT. In this talk, we show how simple gradient-based learning with appropriate constraints on the representation of process, along with restrictions on the gradients can solve QPT. We will demonstrate gradient-based learning of processes for 2- 5 qubits as well as single-mode bosonic systems. We compare our simple approach to existing techniques such as compressed sensing and projected least squares QPT. We also show that using neural networks rather than standard process representations provides no significant advantage which may indicate that good representations of process combined with gradient-based learning might be sufficient for QPT tasks.
