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Abstract Diamond color centers have been widely studied in the field of quantum optics. The negatively charged silicon vacancy (SiV − ) center exhibits a narrow emission linewidth at the wavelength of 738 nm, a high Debye–Waller factor, and unique spin properties, making it a promising emitter for quantum information technologies, biological imaging, and sensing. In particular, nanodiamond (ND)-based SiV − centers can be heterogeneously integrated with plasmonic and photonic nanostructures and serve as in vivo biomarkers and intracellular thermometers. Out of all methods to produce NDs with SiV − centers, ion implantation offers the unique potential to create controllable numbers of color centers in preselected individual NDs. However, the formation of single color centers in NDs with this technique has not been realized. We report the creation of single SiV − centers featuring stable high-purity single-photon emission through Si implantation into NDs with an average size of ∼20 nm. We observe room temperature emission, with zero-phonon line wavelengths in the range of 730–800 nm and linewidths below 10 nm. Our results offer new opportunities for the controlled production of group-IV diamond color centers with applications in quantum photonics, sensing, and biomedicine. 

Deterministic nanoassembly may enable unique integrated on‐chip quantum photonic devices. Such integration requires a careful large‐scale selection of nanoscale building blocks such as solid‐state single‐photon emitters by means of optical characterization. Second‐order autocorrelation is a cornerstone measurement that is particularly time‐consuming to realize on a large scale. Supervised machine learning‐based classification of quantum emitters as “single” or “not‐single” is implemented based on their sparse autocorrelation data. The method yields a classification accuracy of 95% within an integration time of less than a second, realizing roughly a 100‐fold speedup compared to the conventional Levenberg–Marquardt fitting approach. It is anticipated that machine learning‐based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices.