Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.11716 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912802347679744 |
|---|---|
| author | Aziz, Sameen Faryad, Muhammad |
| author_facet | Aziz, Sameen Faryad, Muhammad |
| contents | Quantum channels are not invertible in general. A quasi-inverse allows for a partial recovery of the input state, but its analytical results are found only in a restricted space of its parameters. 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 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. The Kraus operators of the quasi-inverse channel were obtained by performing quantum process tomography on the trained neural network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11716 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-inspired neural networks as quasi inverse of quantum channels Aziz, Sameen Faryad, Muhammad Quantum Physics Quantum channels are not invertible in general. A quasi-inverse allows for a partial recovery of the input state, but its analytical results are found only in a restricted space of its parameters. 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 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. The Kraus operators of the quasi-inverse channel were obtained by performing quantum process tomography on the trained neural network. |
| title | Physics-inspired neural networks as quasi inverse of quantum channels |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2506.11716 |