Sample Efficiency Crisis in Quantum Reservoir Computing: Scaling Analysis on 156-Qubit IBM Hardware and Rigetti Simulation

Sample Efficiency Crisis in Quantum Reservoir Computing: Scaling Analysis on 156-Qubit IBM Hardware and Rigetti Simulation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 December 2025 V2 Latest version Share on Sample Efficiency Crisis in Quantum Reservoir Computing: Scaling Analysis on 156-Qubit IBM Hardware and Rigetti Simulation Author : Daniel Mo Houshmand 0009-0008-2270-5454 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176642526.60787514/v2 131 views 121 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract \(\textbf{$\mathbb{Q}\mid\mathcal{D}\partial\mathfrak{r}\imath\alpha\rangle$}\) presents the largest quantum reservoir computing (QRC) demonstration on real quantum hardware to date, comparing 4-qubit and 156-qubit experimental IBM systems (Heronr3) alongside high-fidelity 9-qubit Rigetti simulation employing the Steinegger-Rth (2025) feature engineering methodology. On time-evolving spectral data, we achieve \(R^{2}=0.754 \) (4Q, 50 samples) and \(R^{2}=0.756\) (156Q, 200 samples), surpassing previous experimental demonstrations of 120 qubits [1] and 108 qubits [2]. To validate QRC generalizability across chaotic regimes, we demonstrate multi-system validation on canonical chaotic attractors: Lorenz-63 ( \(R^{2}=0.756\) , \(\lambda=0.906\) ) and Rossler ( \(R^{2}=0.969\) , \(\lambda=0.071\) ), achieving average \(R^{2}=0.908 \) , across systems spanning a 13× range in Lyapunov exponents. While these results validate QRC methodology on complex dynamics, direct comparison reveals sample efficiency challenges: the 156-qubit system operates at 1.28 samples/feature versus 5.0 for 4Q. Meanwhile, simulated 9-qubit Rigetti Novera achieves \(R^{2}=0.959\) on the same spectral evolution data through polynomial feature engineering (3,375 features, 0.19 samples/feature with ridge regularization), demonstrating that sophisticated readout strategies can overcome data quality limitations. We identify optimal qubit counts of 8-16 for current data availability and provide the first validation of QRC on 156-qubit NISQ hardware, with implications for quantum machine learning resource allocation. Index Terms—Quantum reservoir computing, sample efficiency, turbulence forecasting, NISQ devices, feature engineering, hardware scaling, quantum machine learning, IBM Heron, Rigetti Novera Supplementary Material File (qrc_paper_clean.pdf) Download 3.10 MB Information & Authors Information Version history V1 Version 1 22 December 2025 V2 Version 2 24 December 2025 Copyright This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License Keywords applied mathematics prediction quantum computers quantum machine learning quantum reservoir computing Authors Affiliations Daniel Mo Houshmand 0009-0008-2270-5454 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 131 views 121 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Daniel Mo Houshmand. Sample Efficiency Crisis in Quantum Reservoir Computing: Scaling Analysis on 156-Qubit IBM Hardware and Rigetti Simulation. Authorea . 24 December 2025. DOI: https://doi.org/10.22541/au.176642526.60787514/v2 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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