We lifted 5 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| quantshah/qst-cgan | canonical | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| Discriminator | Ran | quantshah/qst-cgan/qst_cgan/gan.py code served (permissive licence) · get_code("67e33dc775e7dc48") |
| batched_expect | Ran | quantshah/qst-cgan/qst_cgan/ops.py code served (permissive licence) · get_code("e8ff90c54135ae9c") |
| discriminator_loss | Ran | quantshah/qst-cgan/qst_cgan/gan.py code served (permissive licence) · get_code("1d5b2bf29452bf8f") |
| dm_to_tf | Ran | quantshah/qst-cgan/qst_cgan/ops.py code served (permissive licence) · get_code("10db4ead839d8a50") |
| random_alpha | Ran | quantshah/qst-cgan/qst_cgan/ops.py code served (permissive licence) · get_code("fb6d7e9fffc0f957") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Quantum state tomography (QST) is a challenging task in intermediate-scale quantum devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the CGAN framework, two duelling neural networks, a generator and a discriminator, learn multi-modal models from data. We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix. To reconstruct the density matrix, the generator and discriminator networks train each other on data using standard gradient-based methods. We demonstrate that our QST-CGAN reconstructs optical quantum states with high fidelity orders of magnitude faster, and from less data, than a standard maximum-likelihood method. We also show that the QST-CGAN can reconstruct a quantum state in a single evaluation of the generator network if it has been pre-trained on similar quantum states.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2008.03240")
get_code_for_paper("2008.03240")
have("2008.03240")
Connect an agent — have() is free.