HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era

[2402.08475] HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2402.08475 (quant-ph) [Submitted on 13 Feb 2024 ( v1 ), last revised 11 Sep 2025 (this version, v5)] Title: HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era Authors: Muhammad Kashif , Muhammad Shafique View a PDF of the paper titled HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era, by Muhammad Kashif and 1 other authors View PDF HTML (experimental) Abstract: Effective training of Quantum Neural Networks (QNNs) is crucial in the Noisy Intermediate-Scale Quantum (NISQ) era, where noise accelerates the onset of barren plateaus (BPs) and limits scalability. This paper investigates how quantum noise impacts QNN trainability and demonstrates that careful selection of qubit measurement observables can mitigate these effects. We analyze PauliX, PauliY, PauliZ, and a customized Hermitian observable under both global (all-qubit measured) and local (single-qubit measured) cost functions. Our results show that with global cost function, PauliX and PauliY lead to flatter landscapes under noise, while PauliZ maintains training up to $8$ qubits before encountering BPs. The customized Hermitian observable proves most robust, enabling training up to $10$ qubits in noisy settings. For local cost function setting, PauliZ outperforms PauliX and PauliY, maintaining efficiency up to $10$ qubits. These findings highlight the importance of noise-aware observable selection, offering a practical strategy to improve QNN performance and advance quantum machine learning in noisy environments. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2402.08475 [quant-ph] (or arXiv:2402.08475v5 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2402.08475 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/QAI63978.2025.00066 Focus to learn more DOI(s) linking to related resources Submission history From: Muhammad Kashif [ view email ] [v1] Tue, 13 Feb 2024 14:08:18 UTC (2,488 KB) [v2] Wed, 14 Feb 2024 11:29:59 UTC (2,488 KB) [v3] Thu, 15 Aug 2024 11:51:31 UTC (10,436 KB) [v4] Mon, 30 Jun 2025 09:31:06 UTC (2,290 KB) [v5] Thu, 11 Sep 2025 13:21:55 UTC (2,291 KB) Full-text links: Access Paper: View a PDF of the paper titled HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era, by Muhammad Kashif and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2024-02 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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