Evaluation artifacts in reversible quantum reservoir protocols: a withdrawal of our own claims, and an entangled channel that survives the controls

[2603.12303] Evaluation artifacts in reversible quantum reservoir protocols: a withdrawal of our own claims, and an entangled channel that survives the controls Skip to main content Search Submit Donate Log in Quantum Physics arXiv:2603.12303 (quant-ph) [Submitted on 12 Mar 2026 ( v1 ), last revised 27 Aug 2026 (this version, v3)] Title: Evaluation artifacts in reversible quantum reservoir protocols: a withdrawal of our own claims, and an entangled channel that survives the controls Authors: Hikaru Wakaura , Taiki Tanimae View PDF HTML (experimental) Abstract: This replaces v1, which reported a ten-order shot-noise suppression from a quantum reservoir autoencoder and a two-phase protocol reconstructing unseen inputs at MSE ~ 10^-4. We withdraw both claims: under one consistent evaluation the suppression reverses sign (reset channels degrade reconstruction 144x), the two-phase target is invertible in closed form without training, and a polynomial using no quantum features matches the full model. We trace these to four evaluation artifacts; the fourth also invalidates the companion latent code, which never leaves its initialization. What survives the same controls is a joint scrambling channel: injected on one reservoir half, read out on the other, decoded by a readout calibrated once on training messages. Over 32 realizations the held-out error is below chance in 31-32 (permutation p<10^-4) with a shuffled control at chance. Scrambler T-doping is necessary for one-shot injection, while interleaved injection supplies its own non-Cliffordness; a matched classical scrambler decodes the task better, so the transport is not evidence of a quantum mechanism. The access threshold, recomputed on nested subsystems with right-censoring, gives interleaved medians f* = 0.25-0.33; one-shot injection reaches full detectability in only half the realizations at the largest settings. A trained variational ansatz loses to the fixed untrained circuit by 2.6-4.4x. Three extensions pass the same discipline: streaming requires dissipative reset of the accessible half; the decoder does not transfer across the eight keys tested; a receiver holding the key matches the supervised ceiling from self-simulated pairs. A first hardware run decodes at 0.23 with the shuffled control at chance, while coherent device errors partially open the t=0 control. We close with the controls that separate transport from interpolation. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2603.12303 [quant-ph] (or arXiv:2603.12303v3 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2603.12303 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hikaru Wakaura [ view email ] [v1] Thu, 12 Mar 2026 07:05:46 UTC (316 KB) [v2] Thu, 13 Aug 2026 11:44:41 UTC (58 KB) [v3] Thu, 27 Aug 2026 06:45:04 UTC (65 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2026-03 References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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