Quantum Memory in Support Vector Machines
An Empirical Study
Resumen
Quantum machine learning (QML) uses quantum computing to improve the efficiency of learning. A key approach is quantum memory, which enables the coherent storage and reuse of quantum states during computation. Theoretically, quantum memory can exponentially reduce data requirements by preserving correlations between training instances [1, 3], but its empirical validation remains limited. This work examines its impact on a quantum support vector machine (QSVM), comparing its performance with classical and quantum baselines. [...]
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