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Color centers have emerged as a leading qubit candidate for realizing hybrid spin-photon quantum information technology. One major limitation of the platform, however, is that the characteristics of individual color centers are often strain dependent. As an illustrative case, the silicon-vacancy center in diamond typically requires millikelvin temperatures in order to achieve long coherence properties, but strained silicon-vacancy centers have been shown to operate at temperatures beyond 1 K without phonon-mediated decoherence. In this work, we combine high-stress silicon-nitride thin films with diamond nanostructures to reproducibly create statically strained silicon-vacancy color centers (mean ground state splitting of 608 GHz) with strain magnitudes of ∼4×10−4. Based on modeling, this strain should be sufficient to allow for operation of a majority silicon-vacancy centers within the measured sample at elevated temperatures (1.5 K) without any degradation of their spin properties. This method offers a scalable approach to fabricate high-temperature operation quantum memories. Beyond silicon-vacancy centers, this method is sufficiently general that it can be easily extended to other platforms as well. 

As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering. In this paper we extend this notion to hierarchical clustering, where the goal is to recursively partition the data to optimize a specific objective. For various natural objectives, we obtain simple, efficient algorithms to find a provably good fair hierarchical clustering. Empirically, we show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective.