Enhanced Squeezing and Faster Metrology from Layered Quantum Neural Networks
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Quantum Physics arXiv:2512.09137 (quant-ph) [Submitted on 9 Dec 2025] Title:Enhanced Squeezing and Faster Metrology from Layered Quantum Neural Networks Authors:Nickholas Gutierrez, Rodrigo Araiza Bravo, Susanne Yelin View a PDF of the paper titled Enhanced Squeezing and Faster Metrology from Layered Quantum Neural Networks, by Nickholas Gutierrez and Rodrigo Araiza Bravo and Susanne Yelin View PDF HTML (experimental) Abstract:Spin squeezing is a powerful resource for quantum metrology, and recent hardware platforms based on interacting qubits provide multiple possible architectures to generate and reverse squeezing during a sensing protocol. In this work, we compare the sensing performance of three such architectures: quantum reservoir computers (QRCs), quantum perceptrons, and multi-layer quantum neural networks (QNNs), when used as squeezing-based field sensors. For all models, we consider a standard metrological sequence consisting of coherent-spin preparation, one-axis-twisting dynamics, field encoding via a weak rotation, time-reversal, and collective readout. We show that a single quantum perceptron generates the same optimal sensitivity as a QRC, but in the perturbative regime it benefits from accelerated squeezing due to steering by the output qubit. Stacking perceptrons into a QNN further amplifies this effect: in a 2-layer QNN with N_in input and N_out output qubits, the optimal squeezing time is reduced by a factor of N_out, while the achievable phase sensitivity remains Heisenberg-limited, Delta phi ~ 1/(N_in + N_out). Moreover, if the layers are used sequentially, first using the outputs to squeeze the inputs and then the inputs to squeeze the outputs, the two contributions to the response add constructively. This yields a sqrt(2) enhancement in sensitivity over a QRC when N_in = N_out and requires shorter total squeezing time. Generalizing to L layers, we show that the metrological gain scales as sqrt(L) while the required squeezing time decreases as 1/N_l, where N_l is the number of qubits per layer. Our results demonstrate that the structure of quantum neural networks can be exploited not only for computation, but also to engineer faster and more sensitive squeezing-based quantum sensors. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2512.09137 [quant-ph] (or arXiv:2512.09137v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.09137 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Nickholas Gutierrez [view email] [v1] Tue, 9 Dec 2025 21:33:22 UTC (244 KB) Full-text links: Access Paper: View a PDF of the paper titled Enhanced Squeezing and Faster Metrology from Layered Quantum Neural Networks, by Nickholas Gutierrez and Rodrigo Araiza Bravo and Susanne YelinView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 References & Citations INSPIRE HEP NASA ADSGoogle 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)