Optimal Continuous- to Discrete-Variable Bipartite Entanglement Conversion

Quantum Physics [Submitted on 13 Mar 2026] Optimal Continuous- to Discrete-Variable Bipartite Entanglement Conversion Pak-Tik Fong, Ruchir Tullu, Hoi-Kwan Lau Discrete-variable (DV) entanglement is crucial for numerous quantum applications, yet its deterministic generation in many bosonic systems remains experimentally challenging. In contrast, continuous-variable (CV) entanglement can be produced efficiently. We propose two optimal schemes for converting CV bipartite entanglement into DV entanglement using only local operations and classical communication. The first scheme extracts maximally entangled qubit pairs at the theoretically maximal rate, while the second probabilistically produces a maximally entangled qudit pair with the highest average entanglement. In both schemes, we quantify the optimal performance and identify the measurement operators required for implementation. Notably, using only a sequence of binary measurements, our approach can succeed in a finite number of measurement rounds on average, even though the CV resource is infinite-dimensional. Our schemes improve the feasibility of implementing DV-based quantum technologies on bosonic platforms. Comments: 13 pages, 8 figures Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2603.12682 [quant-ph]   (or arXiv:2603.12682v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.12682 Focus to learn more Submission history From: Pak-Tik Fong [view email] [v1] Fri, 13 Mar 2026 05:54:24 UTC (1,585 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 References & Citations INSPIRE HEP NASA ADS 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)