{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quantum-state-tomography-with-conditional","title":"Quantum State Tomography with Conditional Generative Adversarial Networks","arxiv_id":"2008.03240","date":"2020-08-07","proceeding":null,"authors":["Shahnawaz Ahmed","Carlos Sánchez Muñoz","Franco Nori","Anton Frisk Kockum"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2008.03240v2","url_pdf":"https://arxiv.org/pdf/2008.03240v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"quantum-state-tomography-with-conditional","repo_url":"https://github.com/quantshah/qst-cgan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"quantum-state-tomography","task_name":"Quantum State Tomography"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2008.03240","atlas_url":"https://app.syntology.ai/?focus=2008.03240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.03240"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/quantshah/qst-cgan","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"67e33dc775e7dc48","entry":"Discriminator","repo":"quantshah/qst-cgan","repo_kind":"official","path":"qst_cgan/gan.py","file_url":"https://github.com/quantshah/qst-cgan/blob/HEAD/qst_cgan/gan.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"67e33dc775e7dc48"}},{"code_sha256_prefix":"e8ff90c54135ae9c","entry":"batched_expect","repo":"quantshah/qst-cgan","repo_kind":"official","path":"qst_cgan/ops.py","file_url":"https://github.com/quantshah/qst-cgan/blob/HEAD/qst_cgan/ops.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e8ff90c54135ae9c"}},{"code_sha256_prefix":"1d5b2bf29452bf8f","entry":"discriminator_loss","repo":"quantshah/qst-cgan","repo_kind":"official","path":"qst_cgan/gan.py","file_url":"https://github.com/quantshah/qst-cgan/blob/HEAD/qst_cgan/gan.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1d5b2bf29452bf8f"}},{"code_sha256_prefix":"10db4ead839d8a50","entry":"dm_to_tf","repo":"quantshah/qst-cgan","repo_kind":"official","path":"qst_cgan/ops.py","file_url":"https://github.com/quantshah/qst-cgan/blob/HEAD/qst_cgan/ops.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"10db4ead839d8a50"}},{"code_sha256_prefix":"fb6d7e9fffc0f957","entry":"random_alpha","repo":"quantshah/qst-cgan","repo_kind":"official","path":"qst_cgan/ops.py","file_url":"https://github.com/quantshah/qst-cgan/blob/HEAD/qst_cgan/ops.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fb6d7e9fffc0f957"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}