{"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/exploring-channel-distinguishability-in-local","title":"Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks","arxiv_id":"2410.09470","date":"2024-10-12","proceeding":null,"authors":["Sabrina Herbst","Sandeep Suresh Cranganore","Vincenzo De Maio","Ivona Brandic"],"abstract":"With the increasing interest in Quantum Machine Learning, Quantum Neural Networks (QNNs) have emerged and gained significant attention. These models have, however, been shown to be notoriously difficult to train, which we hypothesize is partially due to the architectures, called ansatzes, that are hardly studied at this point. Therefore, in this paper, we take a step back and analyze ansatzes. We initially consider their expressivity, i.e., the space of operations they are able to express, and show that the closeness to being a 2-design, the primarily used measure, fails at capturing this property. Hence, we look for alternative ways to characterize ansatzes by considering the local neighborhood of the model space, in particular, analyzing model distinguishability upon small perturbation of parameters. We derive an upper bound on their distinguishability, showcasing that QNNs with few parameters are hardly discriminable upon update. Our numerical experiments support our bounds and further indicate that there is a significant degree of variability, which stresses the need for warm-starting or clever initialization. Altogether, our work provides an ansatz-centric perspective on training dynamics and difficulties in QNNs, ultimately suggesting that iterative training of small quantum models may not be effective, which contrasts their initial motivation.","url_abs":"https://arxiv.org/abs/2410.09470v2","url_pdf":"https://arxiv.org/pdf/2410.09470v2.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":"exploring-channel-distinguishability-in-local","repo_url":"https://github.com/sabrinaherbst/qnn_local_neighborhood","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"quantum-machine-learning","task_name":"Quantum Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.09470","atlas_url":"https://app.syntology.ai/?focus=2410.09470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09470"}},"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/sabrinaherbst/qnn_local_neighborhood","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1,"unverified":2},"by_repo_kind":{"official":{"samples":3,"ran":1,"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":"1ad23e8b265282b2","entry":"adjust_global_phase","repo":"sabrinaherbst/qnn_local_neighborhood","repo_kind":"official","path":"util.py","file_url":"https://github.com/sabrinaherbst/qnn_local_neighborhood/blob/HEAD/util.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1ad23e8b265282b2"}},{"code_sha256_prefix":"f2141f2f626ac5fb","entry":"adjoint","repo":"sabrinaherbst/qnn_local_neighborhood","repo_kind":"official","path":"random_permutation.py","file_url":"https://github.com/sabrinaherbst/qnn_local_neighborhood/blob/HEAD/random_permutation.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":"f2141f2f626ac5fb"}},{"code_sha256_prefix":"313a3eb1ae1b0100","entry":"diamond_norm","repo":"sabrinaherbst/qnn_local_neighborhood","repo_kind":"official","path":"util.py","file_url":"https://github.com/sabrinaherbst/qnn_local_neighborhood/blob/HEAD/util.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":"313a3eb1ae1b0100"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}