Speaker
Dr
Muhammad Faryad
(Lahore University of Management Sciences)
Description
This work explores the potential of neural networks to find the quasi-inverse of qubit channels for any values of the channel parameters while keeping the quasi-inverse as a physically realizable quantum operation. We introduce a physics-inspired loss function based on the mean of the square of the modified trace distance (MSMTD). The scaled trace distance is used to so that the neural network does not increase the length of the Bloch vector of the quantum states, which ensures that the network behaves as a completely positive and trace-preserving (CPTP) quantum channel.
| Broad physics domain | Quantum Information |
|---|---|
| AI/ML technique(s) to be presented | Physics-inspired neural networks |
Authors
Dr
Muhammad Faryad
(Lahore University of Management Sciences)
Ms
Sameen Aziz
(Lahore University of Management Sciences)
