Low overhead circuit cutting with operator backpropagation

Quantum Physics arXiv:2510.19467 (quant-ph) [Submitted on 22 Oct 2025] Title:Low overhead circuit cutting with operator backpropagation Authors:Debarthi Pal, Ritajit Majumdar View a PDF of the paper titled Low overhead circuit cutting with operator backpropagation, by Debarthi Pal and 1 other authors View PDF Abstract:Current quantum computers suffer from noise due to lack of error correction. Several techniques to mitigate the effect of noise have been studied, in particular to extract the expectation value of observables. One such technique, circuit cutting, partitions large circuits into smaller, less noisy subcircuits, but the exponential increase in the number of circuit executions limits its scalability. Another method, operator backpropagation (OBP) reduces circuit depth by classically simulating parts of it, yet often escalates the number of circuit executions by some factor due to additional non-commuting terms in the updated observable. This paper introduces an optimized approach for minimizing noise in quantum circuits using operator backpropagation (OBP) combined with circuit cutting. We demonstrate that the strategic use of OBP with circuit cutting can mitigate the execution overhead. By employing simulated annealing, our proposed method identifies the optimal backpropagation parameter for specific circuits and observables, maximizing resource reduction in cutting. Results show a 3x and 10x decrease in resource requirements for Variational Quantum Eigensolver and Hamiltonian simulation circuits respectively, while maintaining or even enhancing accuracy. This approach also yields similar savings for other circuits from the Benchpress database and various observable weights, providing an efficient method to lower circuit cutting overhead without compromising performance. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2510.19467 [quant-ph] (or arXiv:2510.19467v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2510.19467 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Debarthi Pal [view email] [v1] Wed, 22 Oct 2025 11:00:37 UTC (848 KB) Full-text links: Access Paper: View a PDF of the paper titled Low overhead circuit cutting with operator backpropagation, by Debarthi Pal and 1 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2025-10 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?)